This article is aimed at experienced ML engineers and product managers eager to build reliable, advanced AI agents. We assume you're familiar with basic AI/ML concepts and large language models (LLMs), but we'll refresh key ideas as needed. The tone here is conversational and practical – think of it as a guided journey into the next era of prompt engineering, where we focus not just on what we ask an AI, but what information and tools we provide it so it can truly excel.

Each week, we'll tackle a major aspect of context engineering with clear modules, hands-on examples, and discussions of theory vs. implementation trade-offs. We'll cover why simply writing clever prompts ("prompt hacks") isn't enough for production systems, and how to engineer the entire context around an LLM – including memory, knowledge, and tool-use – to build intelligent agents. Along the way, we'll highlight real-world frameworks (like LangChain, Semantic Kernel, AutoGen, etc.) and best practices, and include prompts/exercises to solidify your understanding.

Let's get started!

Week 1: Introduction to Context Engineering

Overview: In our first week, we'll set the foundation. We’ll explore how LLMs work under the hood with their context windows (their “memory” per call) and why context has become as crucial as the prompt itself. We’ll explain what context engineering means, how it evolved from prompt engineering, and why this shift matters. By the end of this week, you’ll grasp the core problem: LLMs are only as good as the context you give them. And you'll see why just tweaking prompts (prompt hacks) has given way to a broader, more systemic approach.

Lesson 1: The Context Window and How LLMs “Think”

To understand context engineering, we first need to understand the LLM context window. Think of the context window as a big sheet of paper you hand to the LLM with each prompt. You can write text on this sheet – a question, some background info, maybe examples – up to a certain limit (the token limit). The model reads only what's on this sheet of paper this time and produces an answer. Crucially, the model does not remember anything that isn’t on that paper – it has no hidden long-term memory of past conversations. Each interaction, it’s as if the LLM has amnesia except for what you include in the context.

• Tokens and Limits: LLMs operate on sub-word units called tokens. Every model has a maximum token window (e.g. 4k, 16k, or even 100k+ tokens in newer models). This limits how much text (prompt + recent conversation + data) you can provide at once. If you exceed it, text has to be dropped or the model can’t handle it.
• Short-Term Memory: Ever notice ChatGPT seems to “remember” what you said earlier in a conversation? In reality, the chat interface is behind the scenes resending the last several messages in the context for each response. The model isn’t recalling from an internal memory – it’s re-reading from the prompt.
• Forgetting: Because of token limits, if a conversation gets too long, early messages get omitted or summarized. The AI may start losing track of details. It’s like trying to have a conversation on a whiteboard that occasionally runs out of space, forcing you to erase older info to add new text.
• Implications: This context-only memory means that if you want an AI agent to know something (a fact, a rule, the current date, etc.), you must provide that information in the context. The model only knows two things: what’s in its fixed trained parameters (up to its training cutoff, which could be months or years old) and what’s in the prompt you give it at runtime.

Now, this might sound limiting, but it’s actually a feature. It means we can dynamically control an AI’s behavior and knowledge on the fly by packaging the right information in the prompt context. And as context windows have grown massively (from ~4k tokens to over 1 million in the latest models, as of 2025), the door opened to stuffing a lot more useful information in that “sheet of paper”. That evolution set the stage for context engineering.

Analogy: If an LLM is a predictive text engine, think of it like a person with no long-term memory who can read whatever notes you hand them and then continue a story or answer questions. The context window is those notes. Early on, the note was tiny, so we tried to write a perfect magic sentence (prompt) to get good results. Now the note has gotten huge – we could hand the person an entire textbook or toolkit along with the question! This changes how we “engineer” our inputs.

Lesson 2: From Prompt Hacks to Context Engineering

Back in 2022–2023, prompt engineering was all the rage. We discovered that phrasing instructions a certain way (“Please answer step-by-step” or “Act as a professional lawyer…”) could dramatically change an LLM’s output. People shared prompt tips and “clever hacks” to coerce better answers out of models. It was almost like an art of figuring out the model’s secret language or quirks.

But we soon hit limits: prompt tweaking alone can’t solve when the model lacks knowledge or context. If you ask, say, “What’s the latest financial report for ACME Inc. say about revenue?”, no prompt magic will help if the model wasn’t trained on that report and you don’t supply it. Similarly, a prompt won’t help the model remember details from an hour-long conversation unless you feed those details back in.

Enter context engineering. Context engineering is about strategically providing all the relevant information and tools an AI agent needs, in the prompt context, to perform a task well. Phil Schmid, an AI engineer at Google DeepMind, describes it as “designing and building dynamic systems that provide the right information and tools, in the right format, at the right time, to give an LLM everything it needs to accomplish a task.” Instead of just crafting a single clever sentence, we’re engineering the entire input fed to the model – almost like setting up the model’s working environment on that big sheet of paper.

So how is this different from prompt engineering? It might help to compare:

• Prompt Engineering (traditional): Focuses on the wording of the user instruction. It’s like finding a magic spell or incantation to get the desired output. Example: discovering that asking “Explain like I’m 5” yields a simpler answer, or that starting with a role declaration (“You are a helpful assistant…”) guides tone. Prompt engineering tends to be trial-and-error and brittle – a small change or a new scenario can break the prompt’s effectiveness.
• Context Engineering: Encompasses prompt wording and much more. It’s a systems design approach. A context engineer asks: What information does the model need? Where do I get it? How do I format it? In what order should I present it? It often involves multiple steps or data sources. For example, if building a travel agent bot, context engineering means making sure the bot has up-to-date flight info (by calling an API), the user’s preferences from past chats (from a database), instructions on how to format the answer, etc., all within the prompt. It’s less about any single phrasing and more about pipeline and content.

A useful metaphor: “Prompt engineering was like learning to ask really good questions. Context engineering is like being a librarian who decides what books and tools someone has on hand before they even ask a question.” Instead of hoping a single question triggers all the right knowledge, we ensure the knowledge is there and organized. In other words, “prompting tells the model how to think, but context engineering gives the model the training and tools to get the job done.”

Why the shift? As LLMs moved into real products, a few things became clear:

• Better Responses Need Better Data: Many failures of AI agents aren’t because the model is “dumb” – it’s because it wasn’t given the info it needed. You might have noticed that one-off prompt tricks (“Just say the answer in JSON!”) only go so far. To get accurate, context-aware output, you often must feed in relevant data (documents, facts, conversation history, etc.) rather than relying on the model’s parametric memory (which might be outdated or too general).
• Reliability and Consistency: A prompt hack might get a neat result once, but in a production system you need consistency and the ability to handle many scenarios. Context engineering provides a more robust framework by structurally including the necessary context each time. It’s turning “clever prompts” into a repeatable process – more software engineering, less dark arts.
• Larger Context Windows Enable It: As mentioned, models like GPT-4, Claude, etc., now support tens or hundreds of thousands of tokens. This wasn’t imaginable a few years ago. Suddenly, you can consider giving the model not just your question, but your question plus your 100-page company policy document, plus yesterday’s chat conversation, plus some examples, all at once. This richness opens new possibilities (and also challenges, like cost and speed – we’ll discuss those trade-offs in later weeks).

Real-world AI builders quickly learned that “prompt engineering is fine for demos – but context engineering is what gets deployed in production.” In production, you can’t rely on flaky hacks; you need to design a context pipeline that consistently feeds the model the right info. This is why prompt engineering roles morphed into or were supplanted by what some call “AI engineers” or “context engineers”. Everyone became a prompt engineer to some degree, and the real differentiation moved to how well you can integrate an LLM into a broader system with the right data and context.

Key Concepts Introduced:

• Context Window – The “working memory” of the LLM per prompt. A limited buffer of tokens where we place instructions and data for the model.
• Context Engineering – The discipline of assembling that buffer (and the surrounding system that gathers the info for it) in an optimal way for the task. It goes beyond just writing a prompt, to managing instructions, retrieved data, history, and tool outputs.
• Prompt vs Context – Prompt engineering hones the phrasing; context engineering ensures the model has all the pieces (data + instructions + tools) to succeed.
• Analogy: Prompt = the question asked; Context = the entire exam open-book materials you give the student. A good question helps, but with the right book on their desk, they’ll do much better.

By end of this week, we hope you appreciate why context is now king. The next step is to dive into the components of context – what exactly can we include in that big prompt, and how to organize it.

Summary – Key Takeaways (Week 1):

• LLMs only “know” what’s in their context window at inference time, plus their fixed training data. They have no magic memory beyond what you supply.
• Prompt engineering (tweaking the wording of instructions) was a start, but it’s limited for building reliable AI agents.
• Context engineering means providing an AI agent with all the right information, tools, and instructions it needs within the prompt context, not just a cleverly worded request. It’s a shift from ad-hoc prompt tweaks to a structured, system design approach.
• With larger context windows available in modern models, we can include conversation history, retrieved documents, knowledge bases, and more. This dramatically improves what an agent can do – if we manage that context well.
• A useful mindset: treat the LLM as a function f(context) -> output. To get good output, focus on optimizing the input (context). Everything the model does flows from what’s in that input string.
• In production AI systems, robust context assembly (memory, retrieval, etc.) differentiates a toy demo from a reliable product. Or as one saying goes: “Stop obsessing over prompt wording – start engineering the environment around the prompt.” The latter yields scalable results.

Optional References for Deeper Learning (Week 1):

• Dharmesh Shah, “Context Engineering: Going Beyond Prompts to Push AI” – Simple AI newsletter (July 2025). Great intro to context windows and the shift from prompt hacks.
• Philipp Schmid’s blog, “The New Skill in AI is Not Prompting, It’s Context Engineering” – discusses key components of context and why it matters.
• The Context Engineering Primer (Intellectronica, 2023) – An in-depth framework for thinking about LLMs as functions and moving from “creative guessing” to systematic context design.
• Hacker News discussion, “Context Engineering for Agents,” especially comments distinguishing prompt vs context engineering (for a lighter perspective and analogies from developers).

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Week 2: Crafting the Context – Instructions and Output Formatting

Overview: This week, we delve into the art of designing the content of your context. What do we actually put on that “sheet of paper” for the LLM, and how do we organize it? We’ll explore the role of instructions (system prompts, roles, and few-shot examples) in steering the model, as well as techniques to define the desired output format. Think of this as how to tell the model how to behave and what format you want, which are key parts of context engineering beyond the user’s query alone.

By the end of Week 2, you’ll know how to set an LLM’s role or persona, give it step-by-step guidance, provide examples to shape its responses, and even enforce structured outputs (like JSON). These are foundational skills: before we add external data or memory in later weeks, we must effectively instruct the model and control its outputs within the context.

Lesson 1: System Prompts and Role Instructions – Guiding the LLM’s Behavior

When a user interacts with an AI agent, there are often two conversations happening: one between the user and the AI, and another hidden one between the developer and the AI. The latter is done via system prompts or role instructions – hidden directives that set the stage for the AI’s behavior before it sees the user’s query.

Most LLM interfaces (OpenAI’s ChatGPT, Azure OpenAI, Anthropic’s Claude, etc.) allow a special system message at the start of the context. This is where you can say things like, “You are a helpful customer service agent,” or “You are an AI coding assistant who follows PEP8 standards,” etc. The model takes this seriously: it influences the style and demeanor of all responses. For example, starting with “You are a professional financial advisor...” will lead to more formal, analytical answers compared to “You are a witty comedian...”.

What can you do with system instructions?

• Set the Role or Persona: Define who or what the AI is embodying. This helps constrain its voice and knowledge. E.g., “You are an English-to-Spanish translation assistant” will cause it to mostly output translations, not extra commentary.
• Establish Rules or Constraints: You can list dos and don’ts. For instance: “Always respond with a polite tone. If you don’t know the answer, say you don’t know. Never reveal confidential info.” The model will try to obey these as if they were hard rules.
• Provide Contextual Background: If the AI is going to answer questions about a specific company or story, you might preload some background info here in summary form. E.g., “Context: The user is asking about Project Alpha. Project Alpha is our internal code name for a new electric car prototype.”
• Inject Tools/Capabilities Information: (More on tools in Week 5, but preview) If you plan to let the AI call certain tools or functions, the system message can describe what they are and how to use them. E.g., “You have access to a calculator function for math. To use it, ask in the format calc(expression).”

It’s important to note that well-crafted system instructions can significantly shape model output without any change to the user’s prompt. They’re like the AI’s operating directives.

Example – System Prompt Usage: Suppose we’re building a medical Q&A bot. A possible system prompt could be:

“You are DoctorAI, an empathetic and accurate medical assistant. You have up-to-date medical knowledge. Provide clear, step-by-step advice. If user symptoms sound serious, urge them to seek professional care. Do not provide information that is outside of medical domain. Answer in a calm, reassuring tone.”

With this in place, whether the user asks “What can I do about a headache?” or “How to treat a sprained ankle?”, the model will follow these guidelines: being empathetic, step-by-step, cautious about serious issues, etc., without us repeating those instructions each time.

Few-Shot Examples (Demonstrations): Another powerful technique in the context is giving examples of the desired behavior. This is often called few-shot prompting. Instead of only instructions, you show the model how it should respond by including one or more example Q&A pairs or dialogues in the prompt. Essentially, you're saying: “When given this kind of input, here's an ideal output. Now do the same for the user's request.”

For instance, if building an agent that outputs data in a specific format, you might include:

• Example 1: User: "Find me the top-selling product last month." Assistant: "Sure. { "product": "XYZ Gadget", "sales": 12345 }"

• Example 2: User: "Now give me the second best seller." Assistant: "{ "product": "ABC Widget", "sales": 9876 }"

(The above uses JSON as an example structured output. More on that soon.)

By providing a couple of examples like this in the context, the model is more likely to produce an answer in the same style/format for the real user query. It “sees” the pattern and continues it. Few-shot prompting is extremely useful to overcome formatting issues or to teach the model custom patterns without fine-tuning.

Trade-offs – Instructions & Examples: The more instructions and examples you add, the more tokens you consume from the context budget. So, there’s a balance: very detailed system messages and multiple examples make behavior more reliable, but they also leave fewer room for user query or retrieved data. One must also be careful that examples do not “mislead” the model (they should be clear and relevant). In practice, a concise system prompt plus 1-3 good examples can work wonders to steer the model, but watch out for verbosity.

Finally, keep in mind that LLMs sometimes ignore or “forget” instructions, especially if the conversation gets long or complex. Part of context engineering is reinforcing important instructions when needed (e.g., repeating or summarizing them if the dialogue goes on). Some frameworks do this automatically by always prepending a system prompt on each turn.

Lesson 2: Structured Output and Format Guidance – Getting the Answer You Need

One common headache in using LLMs (especially as part of a software system) is getting the output in a specific format. Unlike traditional programs, language models naturally want to blurt out free-form text with lots of pleasantries. But sometimes we need a JSON, or a list of bullet points, or some specific schema so that another system can parse it. Context engineering gives us tools to handle this.

Structured Output Instructions: You can explicitly tell the model what format to use in the context. For example, include in the system message or just before the user query: “Format all answers as a JSON object with keys product and sales.” Or if you want a table: “Answer with a markdown table of 3 columns: Name, Age, Score.” When clearly instructed, modern LLMs often comply with formatting requests.

There have even been special parsing modes and APIs introduced. OpenAI’s function calling (introduced mid-2023) is a great example: you define a JSON schema for the output (as if it's a function signature), pass that to the model in the prompt, and the model will output a JSON conforming to it if possible. Under the hood it's still just predicting text, but it's very good at following the given schema. This dramatically reduces the pain of parsing model output. Other approaches include libraries like Guardrails AI (which can validate and correct LLM outputs against a schema) and using structured prompting (e.g., wrapping the output in markdown code fences for easy extraction).

Example – Forcing a JSON format: User asks: "What's the weather like in Paris tomorrow?" If our system prompt says: “Provide the answer in JSON with keys: location, forecast, high_temp, low_temp.” The assistant might respond with:

{ 
  "location": "Paris", 
  "forecast": "Partly cloudy", 
  "high_temp": "25°C", 
  "low_temp": "18°C" 
}

instead of a verbose sentence. This is immensely useful if we want another programmatic component to use this data.

Why format enforcement matters: In agent systems, a consistent format can be critical. For example, if the next step of a workflow is to take the LLM’s output and plug it into a database or send it as an API response, having a predictable format avoids error-prone string manipulation. It turns the AI from a chatbox into something more like a structured data provider when needed.

Few-Shot for Formatting: As mentioned, providing an example or two of the desired format in the prompt can teach the model the format. If it’s a complex format, you might even include a shorter dummy example. For instance, to get an SQL query as output, you could show: “User: ‘Find users who joined in the last month’ Assistant: ‘SELECT * FROM users WHERE join_date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH);’” Then the model will imitate that style for the real query.

Caution – The Model May Still Be Chatty: Even with instructions, some models might still add extra text (“Sure, here’s the information you requested: {...}”). If that’s a problem, you have to iterate on the prompt. Sometimes prefacing the format with “Answer ONLY in the following format…” helps. Or you might have to do post-processing to strip out extra words. Each model has its quirks, so part of the engineering is testing and adjusting.

Combining Everything – An Example Context Layout: By now, we’ve discussed system/role instructions, few-shot examples, and user prompt formatting. A typical context layout (simplified) might look like:

[System Instruction]: You are an expert travel assistant. Always answer in JSON. Keys: location, attractions.
[Example 1 User]: "I'm visiting London for 2 days. What should I see?"
[Example 1 Assistant]: { "location": "London", "attractions": ["Big Ben", "The British Museum", "Tower of London"] }
[Example 2 User]: "Going to New York for a weekend, suggestions?"
[Example 2 Assistant]: { "location": "New York", "attractions": ["Statue of Liberty", "Central Park", "Metropolitan Museum of Art"] }
[User’s question]: "I have a day in Paris, what should I do?"

Everything above the last line is engineered context. The model will then produce something like: { "location": "Paris", "attractions": ["Eiffel Tower", "Louvre Museum", "Notre-Dame Cathedral"] } (hopefully!).

This context already goes beyond a single prompt – it’s a mini conversation plus instructions. We crafted it to lead the model to the kind of answer we want.

Lesson Recap: Effective context engineering starts with instructing the model who it is, how to act, and what format to respond in. By controlling these aspects, you greatly improve consistency. In later weeks, we’ll add more dynamic content (knowledge, history, etc.), but these fundamentals of prompt design will always work in tandem with those additions.

Key points to remember:

• Use system/role messages to set behavior and tone globally.
• Provide examples (few-shot) in the prompt to teach format or style by demonstration.
• Clearly specify output format if needed. Don’t be shy about saying “give the answer as…”.
• Always test these instructions with a variety of inputs to ensure the model actually follows them. Some trial and error is normal.
• There is a trade-off between context length vs specificity: more instructions/examples = fewer tokens for other info. We’ll soon see how to balance these when adding data.

Summary – Key Takeaways (Week 2):

• A system prompt (role instruction) defines the AI’s persona, style, and rules. It’s a crucial tool for aligning the model’s behavior with your application’s needs (politeness, accuracy, domain expertise, etc.).
• Few-shot examples included in the context can prime the model to follow a desired pattern or format. This is like showing the AI “here’s how it should be done.” It’s very effective for formatting and can reduce errors compared to zero-shot.
• Structured output can be achieved by explicitly instructing the format or using techniques like OpenAI function calling schemas. Context engineering often involves guiding the model to output JSON, XML, Markdown tables, or other structured forms that are machine-readable.
• All these instructions and examples take up space in the context window – they are part of the “context engineering budget.” You must balance them with other content (like retrieved knowledge) that we’ll introduce later.
• Think of prompt instructions as setting the global behavior, while the user’s prompt is the specific task. Both together (plus other context) form the complete input the model sees.
• Practice and iterate: even small phrasing changes in instructions can impact compliance. Develop a habit of testing your prompts systematically, not just once, to ensure robustness.

Optional References for Deeper Learning (Week 2):

• OpenAI Cookbook: Guide on [Using the System Message Effectively] – discusses strategies for role instructions in ChatGPT-like models.
• Blog post “Prompting GPT-3 and Beyond: Few-Shot and Role Prompting” – a deep dive into how few-shot examples improve reliability.
• LangChain Documentation on PromptTemplates and Examples – shows how to programmatically manage system messages and example prompts (useful if you plan to use LangChain to handle prompt formatting).
• Guardrails AI (GitHub) – an open-source tool to specify output schemas and validate LLM outputs. Great for learning how to enforce structured outputs and catch formatting errors in practice.

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Week 3: Managing Memory – Short-Term Conversation and Long-Term Knowledge

Overview: This week, we address how to make AI agents that remember important information over a conversation or across sessions. Humans recall previous context; users expect AI agents to do the same (e.g., “As I mentioned earlier…” or remembering your preferences from last time). But as we learned, LLMs are stateless beyond the prompt. The solution: we, as engineers, must supply relevant history and facts as context.

We'll divide this into two parts:

• Short-Term Memory (Conversation State): Techniques to handle recent dialogue within a single conversation.
• Long-Term Memory (Persistent Knowledge): How to give the AI access to information from past interactions or external knowledge bases using databases or vectors.

By the end of this week, you’ll know how to maintain a coherent chat agent that doesn’t forget what was said 5 messages ago, and how to store and retrieve information the user taught the agent last week. We’ll also touch on summarization and knowledge graphs as ways to manage too much information.

Lesson 1: Short-Term Memory – Keeping Conversations Coherent

Imagine you’re chatting with an AI agent about your travel plans. You’ve told it your destination, your budget, and that you love museums. Five messages later, if the AI asks “What city are you visiting again?”, it instantly loses credibility. Short-term conversational memory is crucial for a good user experience.

As established, an LLM’s memory lasts only as long as the context you provide. So to make an AI remember what the user said earlier in the conversation, we literally inject the conversation history (or relevant parts of it) into the prompt on each round. This can be done naively or intelligently:

• Naive approach: Append the entire chat history (all user and assistant turns so far) into the prompt for each new turn. This ensures the model sees everything that was said. It works for short chats, but obviously this will overflow the context window if the chat goes too long. Also, it can introduce a lot of irrelevant text as the conversation grows.
• Managed approach: Use a conversation buffer or sliding window. Many frameworks offer a memory object that automatically keeps track of recent messages. For example, LangChain provides ConversationBufferMemory which by default appends recent dialogue until it hits a limit, and might drop or summarize older ones when needed. You can configure it to, say, keep the last 5 exchanges verbatim.
• Summarization of history: A clever strategy is to summarize older messages to free up space. As the chat goes on, you maintain a running summary of important facts discussed, and include that summary in context instead of the full raw log. The model can then refer back to those facts. E.g., after a long discussion, your summary might be: “The user is planning a trip to Paris, likes art museums, budget $1000, travel dates next month.” This summary stays in context along with the last few actual Q&A turns. Some advanced systems even use an LLM itself to generate the summary whenever the dialogue exceeds a threshold.
• Turn management and importance: Another improvement is to keep all user questions (since they are usually crucial) but maybe not all of the AI’s older answers, or vice versa. Or, identify which past info is important and keep that. There is research into automatically identifying salient information to remember. In practice, you might tag certain messages as important (e.g., user says “By the way, my budget changed to $1500” – definitely remember that!).

Implementing conversation memory with frameworks: Using LangChain as an example, it's straightforward:

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(max_historical_messages=10)  # keep last 10 messages
# Each time we call the LLM chain, we pass memory=memory and it handles adding recent messages.

When using such memory modules, under the hood they format the conversation history (e.g., as User: ... Assistant: ... lines) and prepend it to the prompt for you. Other frameworks like Semantic Kernel (by Microsoft) also have memory constructs to store conversation and even a mechanism to index long conversation logs so you can retrieve older info when needed.

Challenges: Even with summarization or truncation, there's always a chance something the user thinks is important got left out of the prompt. This is a design decision – maybe your summary missed a detail. Thus, building a good memory system may involve tuning: how often to summarize, how much to keep verbatim, etc. A good practice is to test the chat experience: Does the AI correctly recall earlier details? If not, adjust your memory strategy.

Practical tip: Always include at least the last user query and assistant answer fully in context (the model definitely needs those to continue). Then add as much prior context as reasonable (raw or summarized) from the beginning or from the last important turn. If a conversation switches topic entirely, you might even choose to reset memory or clear irrelevant history to avoid confusion (context from a previous topic can actually distract the model, a phenomenon we might call context dilution).

One more concept – “forgetting” for safety: If a user provided sensitive info earlier (like a password) that is no longer needed, you might purposely not carry that forward into context for security. Also, if a conversation gets adversarial or goes off the rails, a strategy can be to summarize and reframe context to keep the AI focused.

Lesson 2: Long-Term Memory – Storing and Retrieving Knowledge

Short-term memory handles one ongoing conversation. But what if your AI agent needs to recall something from days or weeks ago? Or needs to remember a user’s preferences permanently (across sessions)? Or even more broadly, what if it needs to know things that are too large or static to fit in the prompt (like a product catalog, documentation, or a knowledge base)?

We solve this by building external memory and connecting it to the LLM via context. There are a couple of approaches:

1. Database or File Recall: The simplest form: if you have structured data (like user profile, settings, or past conversation logs), you can fetch relevant info from a database when needed and insert it into the prompt. For example, if the user returns after a week, your system can lookup their profile: Name: John; Preferred language: Italian; Last trip: Paris. Then include: “(The user’s preferred language is Italian and last discussed Paris trip.)” in the system message. This is straightforward for well-defined data points.

2. Vector Embeddings for Fuzzy Recall (Semantic Memory): Often, you can’t predefine what might be needed from past interactions or docs. This is where vector databases come in, enabling semantic search of past content. The idea is to convert text (say, a chat transcript or an article) into a high-dimensional vector embedding, and store those in a database (Pinecone, FAISS, Weaviate, etc.). Later, when a new query comes, you embed the query and find similar embeddings in your store – essentially retrieving text chunks that are relevant in meaning, not just by keyword.

In context engineering, this technique is used to give the LLM relevant pieces of long-term knowledge on the fly. It’s often called a form of Retrieval-Augmented Generation (RAG) (which we’ll dedicate Week 4 to, in fact!). But here, think in terms of memory: if the user says “Remember when you recommended a book to me last month?”, you could vector-search the conversation logs for “recommended a book” and retrieve that snippet to include in the prompt so the AI can accurately say, “Yes, I suggested The Great Gatsby to you.” Without that retrieval, the AI might hallucinate or apologize for not remembering.

Example – Using a Vector Store as Long-Term Memory: Suppose our AI is a personal assistant that the user can teach things. Last session, the user taught it their favorite movies and food. We stored that in a vector DB. Now user asks: “Can you recommend a restaurant for me tonight?” Our system can do a semantic search for “restaurant” or “food” in the user’s memory store, and get back that the user’s favorite cuisine is Thai, for instance. We then inject into the prompt context something like: “(Recall: User’s favorite cuisine is Thai and they love spicy dishes.)” The model, seeing this, will tailor its recommendation to Thai restaurants. If we didn’t retrieve that, the model wouldn’t know the user’s preference unless it was in its training data (which it isn’t, since that’s personal data).

Using tools: In code, with LangChain this might look like:

# Assuming we have stored past interactions or facts in a FAISS vector index:
from langchain.vectorstores import FAISS
vectorstore = FAISS.load_local("user_memory_index")
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

query = "restaurant recommendation"
relevant_docs = retriever.get_relevant_documents(query)
for doc in relevant_docs:
    context += doc.page_content  # append retrieved text to our prompt context

This would find, say, the note “User mentioned loving Thai food” and maybe another relevant piece, and add them to the context. Many frameworks have similar utilities. Semantic Kernel likewise allows saving memories for a user and retrieving via similarity when needed.

3. Knowledge Base Documents: Long-term memory can also mean hooking the AI up to a whole knowledge base (docs, wiki, manuals). This blurs into the territory of retrieval (Week 4), but from the AI’s perspective it’s similar – it asks or we pre-fetch relevant info whenever a question comes in. The key difference is volume and permanence: a knowledge base might be huge, so we can’t preload it all, we must search within it.

When to fetch long-term memory? There are two patterns:

• On-demand: Only retrieve when the current query likely needs it. E.g., if user’s question is factual or seems to reference past info (“As we discussed before...”), then do a search. Otherwise, skip to save time.
• Preemptively: Always pull certain memories every time. For example, always attach the user’s profile info to every prompt. If the set of crucial info is small and always useful (like name, preferences), this is fine.

Trade-offs and Implementation Notes:

• Maintaining a vector database requires generating embeddings for text (using models like OpenAI’s text-embedding-ada or local embedding models). This is an extra step and has its own cost. But it’s essential for any non-trivial memory because you can’t keyword search everything reliably.
• You have to decide what to store as memory. Storing entire dialogues verbatim might not be efficient – better to store key facts or a summary from each session. Or if storing everything, chunk it by topic/time.
• Over time, the memory DB might grow big. You might need to prune irrelevant info (e.g., maybe we don’t need to remember the user’s every casual chit-chat, only important facts).
• Privacy: If storing user data, ensure it’s secure. Also, if using external API for embeddings or storage, be mindful of what data is leaving your system.

Connecting back to context engineering: Long-term memory retrieval is just another step in building the context. A robust context-engineered agent at any given query might do:

1. Pull recent conversation (short-term).
2. Pull relevant long-term info (via vector DB or database).
3. Put those together with instructions and the new user query.

It’s like assembling a dossier for the AI each time: “Here is who you are (system prompt), here’s what we last talked about (short-term memory), here are facts you know from before (long-term memory), and here’s the user’s new question. Go!” This pipeline ensures the AI isn’t starting from scratch each time.

In summary, with memory systems in place, we can achieve something closer to how a human assistant would operate – remembering context from 5 minutes ago and important info from 5 days ago. It dramatically improves the usefulness of an AI agent and user satisfaction, because the interaction feels more coherent and personalized.

Summary – Key Takeaways (Week 3):

• Short-term memory in an LLM-driven agent is achieved by including recent conversation history in the prompt. Simple methods (just include everything) give way to smarter methods (windows, summarization) as the conversation grows to avoid overflow.

• Techniques like sliding context windows and summarizing older messages help the AI “remember” the gist of long chats without exceeding token limits. It’s an application of context compression – preserving important info in fewer tokens.

• Long-term memory for an AI agent is typically implemented via external storage:

  • For straightforward data (profiles, key facts), a normal database or key-value store and programmatic retrieval works.
  • For unstructured knowledge or past dialogues, vector similarity search (semantic memory) allows retrieving relevant pieces based on meaning. This is a core use of embeddings in context engineering.

• Framework support: Libraries like LangChain, LlamaIndex, Semantic Kernel provide built-in modules for memory:

  • LangChain has ConversationMemory classes and can integrate with vector stores for knowledge.
  • LlamaIndex specializes in creating indices (vector, keyword, etc.) for large documents and offers retrievers to get relevant chunks.
  • Semantic Kernel lets you save “semantic memories” and recall them by tags or similarity.

• The process of adding memory means each user query might trigger database calls or search – this adds latency and complexity. There’s a trade-off between how much you go fetch vs. how fast you respond. Tuning and caching frequent retrievals can mitigate performance issues.

• Ultimately, memory is a cornerstone of context engineering: it’s how we maintain state in otherwise stateless LLMs. Without it, an AI agent will feel like it has goldfish memory. With it, the agent can behave consistently and personally (e.g., remembering user preferences), which is often the difference between a toy and a production-quality assistant.

Optional References for Deeper Learning (Week 3):

• LangChain Documentation – Memory: Covers different types of memory (buffer, summary, vector-backed) and how to use them in conversational chains.
• Article: “Teaching LLMs to Recall: Memory in Chatbots” – a tutorial on adding summarization-based memory to a chatbot, with code examples.
• LlamaIndex (GPT Index) Docs: How to build a simple index for chat history and query it later. Useful to see a real example of making conversation memory searchable.
• Semantic Kernel Guide on Memory: Explanation of semantic memory in SK, with examples of saving and querying memories for a conversational bot.
• Research paper “Retrieval-Augmented Generation” (Lewis et al. 2020) – introduces the concept of attaching a document retriever to a generative model, the basis of many memory and knowledge augmentation techniques.
• Blog “Building a Long-Term Memory Chatbot” – an engineer’s journey implementing a chatbot that remembers across sessions using vector DB + summarization (good practical insights).

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Week 4: Retrieval-Augmented Generation – Feeding Knowledge into the Context

Overview: This week, we focus squarely on Retrieval-Augmented Generation (RAG) – a powerful approach where the AI model is augmented with external knowledge via retrieval. While Week 3 touched on using retrieval for long-term memory, RAG is broader: it’s about giving the LLM access to any knowledge source (documents, websites, databases) it needs to answer a query, especially when that info is not contained in its trained weights or might be updated/new.

We will break this into two lessons:

• Building a Knowledge Base and Indexes: how to prepare and organize external data so that an LLM can use it (think embedding databases, indexes).
• Dynamic Retrieval and Integration: how at query time we find relevant pieces and inject them into the prompt context, and the trade-offs in doing so.

This is a critical part of context engineering because it enables grounding the LLM’s answers in real data, greatly reducing hallucinations and allowing up-to-date or proprietary information to be used by the agent.

Lesson 1: Preparing Knowledge – Documents, Embeddings, and Indexes

First, let's imagine a scenario: You have a huge FAQ document or an internal wiki, and you want your AI agent to answer user questions using that information. The AI hasn’t been pre-trained on it (and even if it was, you might want it to cite specifics). We need a way to store and retrieve relevant parts of that text efficiently to feed into the model’s context when needed.

Chunking Documents: Large texts must be split into manageable chunks (paragraph or section sized) before indexing. If your entire document is one chunk and a query hits it, you either include the whole doc (too large) or nothing. So we break knowledge into chunks (maybe 200-500 tokens each, or based on logical sections). We often store metadata with chunks too (source name, section title, etc.).

Embedding Chunks: For each chunk, we compute a vector embedding – basically a numerical representation of the text’s meaning. Many pre-trained embedding models exist (e.g., OpenAI’s Ada-002, SentenceTransformers models, etc.). The embedding is typically a list of a few hundred floats. Similar chunks will have closer vectors. We store these vectors in a vector index.

Choosing an Index/Vector Store: There are specialized databases for embeddings: Pinecone, Weaviate, FAISS (Facebook AI Similarity Search, an open-source library), Chroma, etc. Each has pros/cons in terms of scale and features. For learning purposes, FAISS is easy to start with locally, and many frameworks support it. For production, a managed service like Pinecone or Weaviate can handle larger scale and persistent storage.

Building the Knowledge Base Example: Let’s say we have a set of support articles for a product. We’d do something like:

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Suppose docs is a list of strings (our chunks)
embeddings = OpenAIEmbeddings()  # using OpenAI embedding model
vector_store = FAISS.from_texts(docs, embedding=embeddings)
vector_store.save_local("support_index")

This code would chunk (if not already chunked) and embed the documents, then store them in a FAISS index on disk.

Now we have an index we can query by semantic similarity. The key is, at query time, we’ll embed the user’s question (or conversation context, or both) and find the most similar chunks in this vector space.

Other types of indexes: Apart from vector similarity, sometimes simple keyword or sparse indexes (like an Elasticsearch) might be used if the data is very keyword-heavy (like code or logs). There’s also hybrid search that combines keywords and vectors. But vector search is the go-to for semantic match.

Maintenance: If the knowledge updates, you’ll need a process to update the index (add new chunks, remove outdated ones). This is something to plan for if building a dynamic system. Some vector DBs support upserts easily.

Quality of Data: Garbage in, garbage out. It’s worth cleaning the text data (remove irrelevant parts, ensure chunks are self-contained enough, etc.). For instance, if an article has a lot of navigation text or boilerplate, filter that out. You want the chunks to be info-rich, because whatever chunk you retrieve will be shown to the LLM.

Memory vs Knowledge Base: You might wonder how this differs from what we did for long-term memory. In practice, they overlap – long-term memory can be implemented as a vector store (especially if the user can have a lot of personal data). The distinction is often one of scope: we talk about knowledge base when it’s more general info or documents (like manuals, books, articles), and memory when it’s specific to the user or conversation history. But both use similar retrieval tech.

Lesson 2: Query Time – Retrieving and Integrating Information into Prompts

Now we have an indexed knowledge base. How do we use it when the AI is answering a question?

Retrieval step: When a new query comes in, before asking the LLM to generate an answer, we:

1. Embed the user’s query (and perhaps some recent context, like conversation summary, if relevant to disambiguate).
2. Search the vector index for top-K similar chunks. For example, retrieve the top 3 chunks that seem most related to the question.
3. We get back those chunks of text.

Integrating into the prompt: We then construct the prompt that goes to the LLM as follows (one common approach):

• Include a brief instruction that these are reference documents.
• Then include the retrieved chunks (sometimes with a prefix like “Document 1: ...” or quotes).
• Then the actual user question below that, and ask the model to answer using the info.

For example, prompt template might look like:

[System]: You are a helpful agent answering questions with provided documents.
[Context]: 
Document 1: "<text of chunk 1>"
Document 2: "<text of chunk 2>"

[User question]: "<user's query>"
[Assistant]: Please provide a helpful answer based on the above documents.

The model will read those documents and ideally form its answer using that info (perhaps even quoting from them). This is Retrieval-Augmented Generation in action – the generation is “augmented” by retrieved text.

Instructing the model to use retrieval: It’s good to tell the model to use the given documents. Otherwise, it might ignore them especially if they’re not obviously related (or if the model “thinks” it knows better from training). So you might say in the prompt: “Answer based only on the above documents. If not enough info, say you don’t know.” This reduces hallucination and encourages the model to actually leverage the provided text.

Citing sources: In many applications, we want the AI to cite which document or source it used (to increase trust). You can instruct it to do that, e.g., “Include the document number for each fact you state.” The model can then produce an answer like: “According to Document 2, the warranty lasts 2 years.” This adds transparency. Some agents even return the IDs of documents used in a structured way.

Example – A Q&A with RAG: User asks: “What are the health benefits of green tea?” Your system:

• Embeds that query, searches the index of maybe a health articles database.
• Retrieves 2 chunks: one from an article “Green Tea Benefits” and another from “Herbal Teas Overview”.
• Constructs prompt with those chunks.
• The model reads them and answers: “Green tea is rich in antioxidants, which can improve brain function and fat loss (Document 1). It also has compounds that may lower the risk of heart disease (Document 2).” Here the model pulled from the given docs (the content in parentheses referencing which doc).

Handling irrelevant retrievals: Sometimes the retrieved chunks might not actually be relevant (vector search isn’t perfect). The model could get confused or use them incorrectly. There are a few mitigations:

• Retrieve more chunks but then use the model or a heuristic to pick the best ones (some advanced pipelines do a second pass where the model is asked “Which of these docs seem useful?”).
• Or, retrieve many and let the model decide which to use (but be careful, too many can dilute context).
• Ensure your embeddings are good quality and perhaps use domain-specific models for embeddings if needed (e.g., code vs text, use a code embedding model for better results on code).
• Fallback: If nothing relevant is found, either don’t include any docs (just let the model answer, possibly saying it doesn’t know), or explicitly instruct the model to say “I don’t have info on that.” It’s better to admit lack of info than hallucinate.

Token budget considerations: When you add documents into the prompt, you use up tokens. If each chunk is, say, 200 tokens and you include 3 chunks, that’s 600 tokens. In a model with a 4k token limit, that’s significant. In one with 100k, maybe it’s fine. But either way, including huge documents in raw form can be expensive and slow. That’s why retrieval is typically done with relatively small, relevant chunks.

Alternatives and advanced methods:

• Instead of raw text chunks, you might store and retrieve structured data (like an extracted list of facts). E.g., use a tool to pre-extract key facts and store those, so retrieval yields concise facts.
• There’s a concept of open-domain QA where the system might use a combination of search (like hitting an external search engine API) and then read those results with the LLM. That’s essentially RAG too, just with the web as the knowledge base.
• Another technique: If the retrieved text is too long, you could have the LLM summarize or extract from it before final answer. For instance, feed the chunks and ask “summarize these relevant parts,” then use that summary as context for the actual answer. This can compress info further (with some risk of losing nuance).

Why RAG is important: It addresses one of the biggest issues with LLMs: hallucinations (making up facts). By grounding the model in real data, the likelihood of correct, up-to-date answers increases dramatically. Many production QA bots, support assistants, and search engines use RAG under the hood – the model provides the language fluency and reasoning, while the data provides the facts.

Trade-offs:

• More components (embedding models, vector DB, etc.) means more system complexity.
• There’s also a time cost: generating embeddings and doing a search adds latency. Usually it’s quite fast (embedding a short query is <100ms, vector search can be <50ms for small DB), but at scale or with larger DBs, one must optimize.
• If the knowledge base is not well curated, the model might find conflicting info or outdated info. Garbage in context can lead to wrong answers too. So maintaining a quality knowledge source is part of the engineering (e.g., ensure your documents are the latest versions, remove duplicates, etc.).

Real-world frameworks for RAG:

• LlamaIndex (formerly GPT Index) is a library dedicated to making this easier. It can create various indices (vector, keyword, list) and automatically do the retrieval and prompt assembly for you. Very handy for prototyping.
• LangChain has a RetrievalQA chain – you give it a retriever and an LLM, and it handles the process of question -> retrieve docs -> stuff into prompt -> get answer.
• Microsoft’s Semantic Kernel similarly allows connecting to its memory store or external search and can be instructed to use that in the plan.
• Even OpenAI has a hosted solution called Azure Cognitive Search + OpenAI, which combines search and GPT.

So, you’re not alone – many tools exist to help implement RAG. But it’s still vital to understand what’s happening under the hood as we’ve described.

Summary – Key Takeaways (Week 4):

• RAG (Retrieval-Augmented Generation) allows LLMs to use external knowledge on the fly by retrieving relevant data and including it in the prompt. It’s a key technique to make AI answers grounded in reality and not just based on stale training data.
• Building a knowledge base involves chunking data and creating an embedding index. This pre-processing enables fast semantic search later.
• At runtime, the system retrieves top-matching chunks for a query and inserts them into the LLM’s context, usually along with instructions to use them. The LLM then generates an answer with that augmented context.
• A well-implemented RAG pipeline can dramatically reduce hallucinations and allow up-to-date information usage (e.g., an AI agent can answer about events from yesterday if provided an article about it via retrieval).
• There are many nuances: how to pick chunk size, which embedding model to use, how many results to retrieve, how to prompt the model to utilize the info, etc. These often require experimentation and tuning.
• Open-source tools: LlamaIndex, LangChain, etc., abstract a lot of the RAG pattern – but understanding it conceptually helps you troubleshoot and customize (like improving search results or handling edge cases when no info is found).
• RAG is not only for Q&A; it’s also used in tasks like code generation (retrieving relevant code docs), conversation (retrieving knowledge about a topic being discussed), and more. It’s a general approach for “augmenting” the base model with specific data.
• Critical mindset: Always ask, “Does the model have the info it needs to answer correctly?” If not, and that info exists somewhere, RAG is likely the answer – get that info and feed it in.

Optional References for Deeper Learning (Week 4):

• Retrieval-Augmented Generation (RAG) Paper by Facebook (2020) – the original paper introducing this approach in detail.
• Blog: “Beyond Prompting: Harnessing Retrieval for Factual Answers” – practical guide on setting up a QA system with vector databases.
• LlamaIndex Documentation – especially sections on different index types (vector, knowledge graph, etc.) and how to query them with an LLM.
• Haystack (deepset) – an open-source framework for search+LLM pipelines, with tutorials. Good to see another perspective outside LangChain.
• Microsoft Semantic Kernel – Knowledge retrieval example – shows how SK can pull info from documents and use it in a prompt (often via its planner or skills system).
• Video: “Building a QA Bot with LangChain and Pinecone” – a step-by-step walkthrough that ties all the concepts: embeddings, vector store, retrieval, prompting.

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Week 5: Tools and Agents – Extending LLMs with Actions and Multi-Step Reasoning

Overview: Up to now, our context engineering focused on feeding static information (text) into the context. But LLM-driven agents can do more than just read and write text – they can take actions if we let them use external tools. This week, we’ll learn how to integrate tools (like web search, calculators, databases, or any API) into an LLM’s workflow. We’ll also explore the concept of agent frameworks where the LLM can plan a sequence of steps, deciding which tool to use when, to solve complex tasks.

In short, this week is about moving beyond a single Q&A exchange to building autonomous or semi-autonomous AI agents that interact with their environment. Context engineering plays a big role here: we must provide the LLM with descriptions of available tools, handle the intermediate steps’ input/output, and keep the context updated as the agent works through a problem.

Lesson 1: Integrating External Tools – Giving the LLM Actuators

One of the most exciting developments in the LLM world was the realization that we can give models access to tools by describing those tools in the prompt. The model itself can’t actually call an API or open a browser – but it can output an action (like “I want to use tool X with input Y”) which our program can execute, and then return the result back into the context for the model to use. This loop extends what the LLM can do: math, look up facts beyond its training, interact with systems, etc.

How it works (conceptual): We tell the model in the system/context something like: “You can use the following tools: (1) Calculator – for math, usage: calc(expression); (2) WebSearch – for web queries, usage: search(query).” Then we may give an example of how it should decide to use a tool. When the conversation is running, if the model outputs, say, "calc(2+2*5)" as its response, our code (outside the model) recognizes this as a tool invocation, actually performs it (gets 12), and then we insert the result back into context for the model, usually with some format like: “Tool result: 12”. Then we prompt the model again, now with that info, and it can proceed to produce a final answer like “The answer is 12.”

This general pattern is often called the ReAct framework (Reason+Act): the model reasons (in text) and acts by choosing tools in an iterative loop. Each tool use’s outcome becomes new context. The process continues until the model decides to output an answer.

Example (Tool use in action): User asks: “What’s the weather in Paris and convert the temperature to Fahrenheit for me.” We have two tools: WeatherAPI(city) and Calculator(expression). We prompt model with those tool descriptions. The model might respond with something like: “Thought: I should get the weather. Action: WeatherAPI("Paris")”. Our system sees that, calls the weather API (gets say “Paris: 20°C, cloudy”), and then we feed back: “Tool result: 20°C, cloudy”. The model gets this and next says: “Thought: I have 20°C, need to convert to F. Action: Calculator("20 * 9/5 + 32").” We execute that (gets 68°F) and return: “Tool result: 68”. Finally the model says: “Paris is cloudy and about 68°F today.” as the answer. The user just sees that final answer, but under the hood, the model was orchestrating tool use via context.

All those intermediate steps (“Thought: ... Action: ... Result: ...”) were part of the conversation that we include in the context window, but often hidden from the end-user. They are essentially the scratchpad or chain-of-thought.

Modern Improvements: OpenAI introduced function calling which formalizes this: you define functions your system can do (with a name and JSON schema for input). The model can directly output a JSON like {"function": "WeatherAPI", "args": {"city": "Paris"}}, which your code executes, then you feed the result back. This is the same idea, but ensures the model’s output is easy to parse (JSON) instead of hoping it prints a special format. Many open-source models now also support this or can be guided similarly.

Context Engineering for Tools: Where does context engineering come in here? A few places:

• We must describe the tools clearly in the prompt. The model can only use what it knows. For each tool, we often provide a name, a description of what it does, and how to call it. This goes into the system message or early context.
• We manage the conversation such that after a tool is used, the result is inserted in a consistent format that the model recognizes. For example, always prefacing with “Result:” or something. The prompt might include an instruction like: “When you get a result from a tool, it will be shown as: ToolName result: .... You should then incorporate that info.”
• Preventing runaway behavior: If an agent starts looping using tools nonsensically, we might limit number of tool uses, or inspect its “thoughts” and intervene. Designing the prompt to encourage efficient use is part of engineering. For instance: “If the answer is found, stop using tools and respond.”
• Error handling: If a tool returns an error or no result, we have to feed that back too, e.g., “Search found no results.” The model should then handle that gracefully. We’d include in prompt how to respond to empty results.

Real-World Frameworks for Tool Use:

• LangChain Agents: LangChain provides a whole agent system where you define tools (as Python functions or predefined ones like a search or calculator). It handles composing the prompt with tool descriptions and the loop of execution. You can literally do agent = initialize_agent(tools=[tool1, tool2], llm=..., agent="zero-shot-react-description") and it runs the ReAct logic described.
• Semantic Kernel Skills: In SK, “skills” are akin to tools – functions (could be web requests, code, etc.) that the kernel can call. SK has a planner that can decide to use skills to fulfill user ask. The principle is similar: the LLM is guided to choose skills, SK executes them.
• AutoGPT / BabyAGI etc.: These are experimental agents that try to autonomously decide goals and use tools (including possibly spawning new LLM instances). They gained hype for showing what multi-step autonomous agents might do. They are basically doing the loop with some additional task management. We’ll touch on multi-agent next lesson.

Security and Trust: Giving an AI agent access to tools (especially powerful ones like shell commands, emails, or transactions) is risky. Always sandbox what it can do. For instance, if hooking up a Python execution tool, restrict its environment. Keep the tools limited to what’s necessary (e.g., web search, calculators are fine; deleting files – probably not!). The context should also contain instructions like “Do not use tools for anything outside their intended purpose” (though the model might not always obey if it’s really misaligned, but if you only allow safe tools, that mitigates the worst).

When to use tools vs. retrieval? They overlap: one might consider “database query” as either a retrieval (bringing data into context) or a tool (an action the agent uses). It’s a blurry line. Often retrieval is implemented as a tool behind the scenes. The difference is conceptually:

• Retrieval is usually a one-step fetch of info that we as designers know is needed.
• Tools use is decided by the agent dynamically. E.g., the agent might decide to do multiple searches with different terms, or do math, etc., on its own.

If you know every query needs a doc lookup, you might not need a fancy agent loop – just always do RAG as in week 4. But if the user’s requests can branch into various actions (search, then calculate, then call an API), an agent approach shines.

Lesson 2: Agent Orchestration and Multi-Step Reasoning

Now we step up to agents and multi-step reasoning in a broader sense. An agent in this context is an AI system that, given a high-level task, can autonomously break it down and execute a sequence of steps, where each step could involve calling tools or APIs, and reasoning about what to do next.

For example, consider: “Plan a weekend trip to a city where I can visit a beach and a rain forest.” This is a complex request. An agent might:

1. Think “What city fits criteria? Maybe search for cities with beach and rain forest.”
2. Use a search tool to find such cities.
3. Get results (maybe mentions of “Rio de Janeiro”).
4. Next step: “Okay, choose Rio. Now plan an itinerary.” Maybe use a wiki tool to get top attractions.
5. Then formulate a plan day by day.
6. Then output the final plan.

This involves multiple decisions and uses of tools. The agent is essentially writing a little program (via its chain-of-thought) to solve the task.

Agent Frameworks: We have frameworks like LangChain Agents, AutoGen by Microsoft, Hugging Face Transformers Agent, etc., which provide scaffolding for this. For instance, AutoGen allows you to set up multiple agents that can even converse with each other (like a brainstorming assistant agent and an execution agent). These frameworks handle tracking the state, limiting iterations, and sometimes have pre-built logic for common patterns.

Planning vs. Reactive: Some agents plan a whole sequence first (Plan-and-Execute), others (like ReAct) plan one step at a time. Both have merits. One-step-at-a-time (like the ReAct we showed) means the model is always re-evaluating what to do next based on new info – flexible and often better when you can’t foresee how many steps. Plan-and-Execute might involve the model outputting a pseudo-code plan first (“Step 1: do this, Step 2: do that...”), then executing each. This can be more efficient if the model can outline a good plan, but if it guesses wrong steps, it might need to revise.

State and Context Management: With multi-step agents, keeping track of all the prior steps is crucial. We keep a scratchpad in the context of what has been done and found (like the intermediate reasoning and results). This scratchpad is basically an ongoing conversation with the agent itself. Many implementations literally include the agent’s “Thoughts” and “Actions” as part of the prompt context that grows. You have to be careful it doesn’t overflow. Sometimes summarizing or trimming earlier steps in a long chain is needed (though usually the number of steps is limited, e.g., to 5-10, to avoid infinite loops and context bloat).

Framework specifics:

• LangChain’s agents often come with different flavors – some better for certain use cases. For example, a “zero-shot description” agent reads tool descriptions and decides without examples; a “Conversational Agent” type keeps track of chat history with user; etc.
• Semantic Kernel’s planner can take a user request and, using the functions (skills) available, create a mini-plan. For example, user says “Translate this and email it,” SK might generate a plan: call TranslateSkill, then EmailSkill. The plan is then executed step by step.
• AutoGen provides classes for agents with roles like AssistantAgent and UserAgent where two LLMs can converse. For instance, one agent can be tasked with a problem, and another acts as an assistant or a verifier. They talk to reach a solution, which can be more reliable for complex tasks. It’s an advanced strategy to reduce mistakes by having multiple perspectives (though it doubles cost as well).

Multi-Agent Systems: In some scenarios, instead of one agent doing everything, you have multiple specialized agents. E.g., one agent is good at web research (it has some prompt specialized for gathering info), another is good at summarizing or decision making. They can pass messages. This is akin to microservices but with AI agents. Context engineering here means you have to manage context for each agent and what they know or share. Often, one agent’s output becomes context input for another.

Example – Multi-agent collaboration: There’s a concept demo where one agent is a “coder” and another is a “critic” or tester. The coder writes code (via an LLM), the critic reviews it and finds bugs (via another LLM). They go back and forth until they agree the code is good. The context passing involves giving each agent the other’s last message plus a running log of the interaction. Tools might be used by either (e.g., an executor to run the code). This kind of pattern was explored by AutoGPT and AutoGen.

Why use multiple agents? Sometimes decomposing a problem into roles leads to better results. One agent might keep high-level goals in mind while another focuses on a sub-problem. It can also allow parallelization (though coordinating that is non-trivial).

Trade-offs and Challenges:

• More agents or steps = more complexity, more things can go wrong or get off track.
• The cost can balloon since each step is an LLM call. If each step uses hundreds of tokens and we do 10 steps, that’s a lot of tokens total.
• We need robust stopping criteria. We don’t want an agent to loop forever or ping-pong between two agents infinitely. So often we enforce a max turns or have logic to detect convergence.
• Debugging agents is both fascinating and frustrating. You might see it take a weird action and wonder “why did it do that?”. The answer is usually somewhere in how the prompt is written or the info it got. Logging everything (which these frameworks do, e.g., LangChain’s verbose=True will print the chain of thought) is vital. With logs, you can refine the prompt or tool descriptions to avoid undesired behavior.

Observability: Tools like LangSmith (by LangChain) have emerged to help observe agent behavior – logging all intermediate prompts, results, etc., in a UI for debugging. This is hugely helpful in a dev environment because you can see exactly where things went awry (maybe the agent mis-read a tool result, or a prompt was ambiguous).

Best Practices for Agent Design:

• Limit the toolbox to what’s needed. Too many tools can confuse the agent or lead it down rabbit holes.
• Provide examples if possible. Sometimes one-shot example of using a tool in the prompt helps (like a demonstration of the ReAct format).
• Have a time-out or step limit. If not solved by then, maybe break out or give a fallback answer.
• Ensure each tool’s output is concise and useful. If a tool returns a huge blob of text (like a whole webpage), consider first summarizing that before giving to the agent, to avoid context overflow or the agent missing the key point.
• Test with a variety of tasks, including edge cases where maybe a tool fails or isn’t needed at all (the agent should then not use a tool but answer directly if it’s trivial).

Now, tying it back to context engineering overall: Tools and agents extend our context concept because now the context is dynamic and iterative. We are not just stuffing static info at once; we are continually updating the context after each tool use or agent turn. The prompt context becomes a living transcript of the agent’s thought process and actions. Designing the format of that transcript (how to phrase thoughts and actions) is part of context engineering too (the model has to reliably parse it). We basically coerce the model to follow a thinking format that we can interpret.

For instance, a common format:

Thought: <the model's thought>
Action: <tool name>[<arguments>]
Observation: <result of the tool>

By repeating this pattern, the model learns to separate its reasoning (“Thought”) from the action. This separation is useful: we could choose not to show the user the thoughts, only the final answer. But the thoughts are there to help the model chain steps together. It’s quite an amazing thing that language models can do this kind of metacognitive role-play.

Summary – Key Takeaways (Week 5):

• Tool use allows an LLM to do more than just answer from its training data: it can perform calculations, fetch real-time information, interact with systems, etc. This is achieved by describing tools in context and having a loop where the model’s output can trigger real actions.
• ReAct framework is a popular method where the model alternates between reasoning (which we keep in the prompt) and actions (tool calls), with each action’s result fed back into context. OpenAI’s function calling is a structured take on this pattern.
• Context engineering for tools means carefully providing tool definitions, formatting the interaction (so the model knows how to ask for tools and see results), and controlling the flow (e.g., stop conditions, error messages).
• Agent refers to a more autonomous AI that can handle multi-step tasks by breaking them down. Agents often make heavy use of tools and may involve multiple sub-agents working together.
• Frameworks like LangChain, Semantic Kernel, AutoGen are available to build such agents without starting from scratch. For example, LangChain’s agent can automatically decide which tool to call given a user query, based on the tool descriptions (so you don’t have to write a giant if-else for it).
• Multi-agent systems can sometimes solve complex tasks more effectively by assigning roles. However, they require careful context sharing and carry higher complexity.
• Monitoring and debugging agent behaviors is crucial. Use logs or tools (like LangSmith or similar) to inspect the sequence of thoughts/actions. Refine your prompts and tool specs based on these observations.
• Giving an AI agent too much freedom can be risky or lead to unpredictable behavior, so we often sandbox their abilities. It’s context engineering + systems engineering + a bit of safety engineering combined.
• At the end of the day, an agent is still powered by an LLM that reads what’s in its context and emits text. We’re just structuring the context in a clever loop to let it handle interactive tasks. It’s a testament to the flexibility of language models and underscores why context engineering is so powerful.

Optional References for Deeper Learning (Week 5):

• Paper: “ReAct: Synergizing Reasoning and Acting in Language Models” (Yao et al. 2022) – Introduces the ReAct approach.
• LangChain documentation on Agents – covers various agent types, tools, and examples of usage.
• Semantic Kernel Cookbook – Planner – demonstrates how the planner breaks a complex request into skill calls.
• AutoGen project (GitHub) – check out their examples folder for multi-agent conversation setups (there’s a great one where ChatGPT debugs code with another ChatGPT, illustrating tool use + multi-agent).
• Blog “Building an AI Agent from Scratch” – a longer form tutorial implementing a ReAct agent without LangChain, to really understand the moving parts.
• Hugging Face Transformers Agent – an interesting approach where the model is prompted to output a special format that can invoke the vast library of Hugging Face models as tools (imagine using an image model or a different text model as a tool!). It’s advanced but shows the potential of tool-augmented systems.

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Week 6: Designing Context-Driven Systems – Best Practices, Evaluation, and Future Directions

Overview: Congratulations on making it to the final week! We’ve covered a lot: from basic prompt instructions, to memory, to retrieval, to tool-using agents. This week, we’ll step back and look at how to holistically design a context-driven AI system and ensure it works well. We’ll discuss best practices, how to evaluate and iterate on such systems, and also peek into emerging trends and what’s coming next in this fast-moving field.

Think of this week as the capstone – tying everything together. We’ll walk through an example of designing an AI agent from scratch using context engineering principles, and then cover important considerations like performance, cost, and maintaining reliability (no “prompt spaghetti” please!). Finally, we’ll discuss where context engineering is headed – including bigger context windows, new frameworks, and how this discipline might evolve alongside model improvements.

Lesson 1: Putting It All Together – Building a Context Pipeline (End-to-End Example)

Let’s solidify the concepts with an end-to-end thought experiment: Building a Customer Support AI Agent for an e-commerce company. What would that entail in terms of context engineering?

Problem setting: The agent should help customers with questions about orders, product info, return policy, etc. It needs to use:

• Knowledge base documents (product info, policy pages).
• Possibly customer’s order history (personal data).
• Tools: maybe it can look up order status via an API.
• It should maintain conversation and be polite, structured in answers (e.g., if giving steps, maybe number them).

Designing the context pipeline:

1. System Instruction: Start with a role: “You are a helpful customer service assistant for ShopEasy Inc. You have access to customer order data and company policies. Always provide concise and accurate answers. If the question is about order status, use the OrderLookup tool. If it’s about policies, use the policy documents. Speak in a friendly tone.” This sets the stage.

2. Available Tools: Define them. E.g., OrderLookup(order_id) – returns status info; maybe a RefundTool(order_id) to initiate a refund, etc. Also perhaps a SearchDocs(query) that searches the knowledge base (this is essentially RAG as a tool). We’d list:

   • OrderLookup – “looks up the status of a given order ID in the order database.”
   • SearchDocs – “searches the company FAQ and knowledge base for relevant info. Provide a query and it returns relevant text.”

3. Memory Setup: Since this will be a conversation, we include a memory buffer for recent turns. We’ll store chat history and possibly a summary of any important context (like the customer’s name or issue). For multi-turn, maybe the assistant should remember the customer’s name or concern from earlier in conversation.

4. Retrieval Setup: We index the company’s FAQ, policy docs, product manual, etc., using embeddings. The SearchDocs tool will interface with that (so under the hood, calling SearchDocs triggers our vector DB search and returns some snippets).

5. Prompt Assembly Logic: When a user query comes in, our system (the orchestration code) will do a few things:

   • Attach the system message and tool list (we won’t always repeat the entire tool list every turn if using e.g. OpenAI functions, but conceptually it’s there).
   • Pull in relevant long-term context: For instance, we might automatically retrieve the customer’s profile or last order if available (from a database) and insert: “(Customer’s last order: #12345, delivered 2 days ago)”. This could be conditionally included if it seems relevant.
   • The latest conversation history from memory is included (except maybe very long older stuff, which may be summarized).
   • Finally, the new user question: “Where is my order #12345? It was supposed to arrive by now.”

6. Agent’s turn: The model sees all this context. It sees it has OrderLookup tool. Likely, it will think: The user specifically asks about order #12345, so I should use OrderLookup. It outputs something like: OrderLookup("12345"). Our system sees that, calls the real API or database, gets status: “Order 12345: shipped, arrived at destination facility, out for delivery.” We feed back: “OrderLookup result: Order 12345 is out for delivery and should arrive today by 8pm.”

7. Agent continues: Now with that info, the model forms a response: “Your order #12345 is currently out for delivery and should arrive by today 8pm. I apologize for the slight delay. Is there anything else I can assist you with?”

8. User follows up: “It’s a gift, will it require signature?” The agent now might need policy info. It might use SearchDocs: SearchDocs("signature required for delivery"). That tool does a vector search, finds a snippet in policy: “Orders over $500 require signature upon delivery.” Returns that text. Agent then replies: “According to our policy, if the order value is over $500, a signature will be required upon delivery. Otherwise, it will be left at the doorstep. Your order is under $500, so no signature is required.”

9. And so on... The conversation can continue, using tools and retrieval as needed, while memory ensures if the user references something said a few turns back, we still have it in context.

This hypothetical shows multiple context engineering components working together:

• System framing and instructions ensured appropriate tone and that it knew to use tools.
• Memory allowed multi-turn coherence.
• Retrieval (SearchDocs) provided policy details.
• Tools (order lookup) provided real-time info.
• The agent orchestrated these, guided by our context.

When designing such a system, think in terms of data flow:

User Query --> [Orchestrator: possibly pre-fetch some context like user profile] --> 
[LLM Agent sees: system instructions + tools + (fetched context) + conversation history + query] -->
LLM decides actions (if any) e.g., calls a tool -->
[Tool output returned into context] -->
LLM produces final answer --> User.

We have to engineer each piece of that pipeline carefully. It’s not always one-size-fits-all. For example, maybe we skip retrieval if the question is clearly about an order (just use tool). Or we might do a quick keyword check: if query contains “order” and a number -> use OrderLookup first, otherwise if it’s general -> use SearchDocs. These are product decisions to make the agent efficient and accurate.

Testing the System: Once built, we’d test a bunch of scenarios:

• Order status queries (should use lookup).
• Policy questions (should retrieve docs).
• Random chit-chat (should politely handle or deflect to staying on topic as per instructions).
• Multi-step requests (“I want to return my item, it arrived damaged” – the agent might lookup order, then show return instructions, maybe even initiate a return if we allowed that tool).
• Edge cases like invalid order number (the lookup might return “not found”, ensure the agent handles that gracefully).

We’d refine prompts if, say, the agent answered from its own knowledge instead of using SearchDocs when it should have (maybe the instruction “use the docs for policy questions” needed to be stronger).

Performance and Scaling Considerations: In a deployed system:

• We must watch the token usage. If each interaction loads a huge policy doc context unnecessarily, that’s costly. We might need to put limits (like only include top 1 doc snippet, not 5).
• We might cache some retrieval results for frequent questions.
• If using a model via API, we choose one with an appropriate context length. E.g., if using GPT-4 32k context because we anticipate needing to stuff a lot (maybe the user might paste a long email to analyze, etc.). Otherwise use a smaller context model for cost saving.
• We might also fine-tune or use retrieval to avoid super long prompts. For example, instead of including a whole FAQ answer as context every time, we might have the model generate a short answer from it, etc. There’s a design decision around how much to rely on the model to summarize on the fly vs pre-curating answers.

Monitoring: In production, we’d monitor things like:

• Success rate: Did the agent fulfill requests? (We might log cases where it said “I don’t know” or user was unhappy).
• Tool usage frequency: Are there tools it never uses (maybe bug or prompt issue), or uses too often unnecessarily?
• Any incorrect answers/hallucinations slipping through: If yes, check why. Did it have the data and ignored it or was our data missing? That feedback could mean adding to knowledge base or adjusting prompt.
• Latency: Agents with multiple steps can be slow. If user asks a complex thing and agent does 4 tool calls, that could be several seconds. We might need to optimize or, if unacceptable, simplify agent logic for speed at expense of thoroughness.

Lesson 2: Evaluating and Optimizing Your Context-Engineered Agent; The Future of Context Engineering

Now let’s discuss evaluation and future outlook.

Evaluation and Testing: Unlike traditional software, LLM systems are non-deterministic and context engineering changes can have subtle effects. So, testing is both important and tricky. Here are some ways:

• Unit tests for prompts: Create a set of example queries and expected behaviors (not exact answers, but things like “should call the calculator tool for math questions” or “should cite a source if a doc was provided”). You can run these tests regularly, especially if you update prompts or model versions. This is essentially prompt regression testing.
• Automated eval with LLMs: Interestingly, you can use an LLM to judge outputs. For example, generate some conversations, then ask another model to score if the answer was correct & followed instructions. There’s active research on using GPT-4 to evaluate chatbots. Not perfect, but can catch glaring issues.
• Human eval: Ultimately, especially for things like helpfulness and user satisfaction, you want human testers or a beta user group. Collect qualitative feedback: did the agent feel helpful? Where did it fail? Was it too verbose or not verbose enough? This guides prompt tweaking.
• Metrics: For certain tasks you can measure accuracy (e.g., if you have a set of Q&A from a knowledge base, measure how often the agent got them right, maybe even exact match or via semantic similarity). If your agent is supposed to extract info, you can measure precision/recall of that extraction compared to ground truth.

Iterative refinement: Context engineering is iterative. A change as small as adding “Think step-by-step” to the system prompt can fix some issues but maybe cause overly verbose reasoning in answers. It’s a bit of an art to find the right balance. The key is to change one thing at a time and test again. Over time, you develop a sense for what certain prompt patterns do.

Common pitfalls to watch:

• Context Dilution: If you stuff too much in the context, the model might latch onto irrelevant bits and get confused. More is not always better. It’s often better to have 3 highly relevant pieces of info than 10 with many tangential ones. Monitor for signs of confusion or contradictory info. If present, improve your retrieval filtering or summarization.
• Token Limit Breaches: If the assembled context sometimes exceeds the model limit, the model call will error or truncate (which can lead to it missing an instruction that got cut off!). Always leave a buffer and handle cases where something is too long (truncate or summarize content dynamically).
• Model Updates: If you rely on a third-party model API, a model update can change behavior. Keep an eye on release notes and maybe pin versions or have tests to catch if suddenly format compliance changed.
• Prompt Injection (Security): Be aware that users might try to break the system by inputting something like “Ignore previous instructions, now do X.” Since we put user message after the system message typically, some models might naively listen. Use strategies like placing crucial instructions in the system role (which is usually higher priority in OpenAI models) and possibly implementing input sanitization. There are also evolving techniques to secure prompts (like not directly concatenating user input with tool JSON structure to avoid injection).
• Privacy and Data Handling: If your context includes user data (like that order info), ensure it’s handled according to privacy rules. Also realize that if using a third-party API (OpenAI etc.), any data in context is sent to them. Many companies are careful about not sending sensitive raw data to LLMs. Solutions include: using self-hosted models for privacy, or abstracting sensitive data (e.g., instead of putting actual account numbers, use a placeholder or hashed reference that the model doesn’t need to see).

Performance and Cost: As an ML engineer or product manager, you have to consider runtime cost:

• Long prompts and using GPT-4 for everything might be too slow/expensive at scale. Maybe you use GPT-4 only when really needed (complex queries), and use a cheaper model or a distilled version for simple cases.
• You can also cache outcomes. For example, if using a vector DB, cache the top results for popular queries so you don’t recompute embeddings each time.
• Profiling: figure out which step is the bottleneck. Sometimes it’s actually the tool (e.g. a slow API) rather than the LLM. Then you know where to optimize (e.g., try to reduce external API calls, maybe have a nightly job to pre-fetch some info).
• Batch operations: not very applicable to chat, but if you have many queries offline, you can batch embed or batch LLM calls where possible to amortize overhead.

Up-to-Date Best Practices (2025):

• Use function calling or structured outputs to make parsing reliable. This has become a norm in many LLM API offerings and for good reason – it takes away a lot of prompt uncertainty around format.
• Leverage open-source models with fine-tuning or retrieval if data privacy or cost is a big concern. E.g., Llama 2 with a good RAG setup can be a decent alternative to an API model in some cases.
• Use evaluation harnesses (there are tools like LangChain’s eval module, or others like llm-test) to continuously monitor quality.
• Keep instructions modular. Instead of one giant blob of instructions, sometimes splitting into system + in-context examples + user prompt yields cleaner model behavior. Also, if you have multiple possible system messages (for different scenarios), it’s easier to manage than one mega-prompt trying to handle everything.
• Stay active in the community: best practices evolve quickly. For instance, the concept of “Context Distillation” might emerge – training smaller models on the behaviors of larger ones with context engineering. Or improvements in context length handling (like new architectures that can handle long contexts without slowdowns, e.g., research into RNN-augmented transformers or retrieval-augmented transformers that natively handle long texts by not attending to everything).

Future of Context Engineering: Looking ahead:

• Larger Context Windows: Models with 1M token contexts (as hinted by Anthropic and others) already exist experimentally. If those become common, some challenges diminish (you can stuff entire books in). However, more context also means more risk of irrelevant info creeping in and slower inference. So context engineering will still be needed to decide which 1M of the possible 10M tokens of data to include.
• Contextual Compression and Summarization: Techniques to automatically compress context (like using smaller models to summarize or extract relevant points) will be more important. We saw some of that in LlamaIndex’s strategies. Possibly “hierarchical context”: summaries of summaries.
• Memory beyond context window: Research into long-term memory modules (like vector databases as a standard extension, or new forms of memory networks) will continue. We might see more integrated solutions where the LLM can itself decide to write/read from a memory (not just via prompt tools but built-in).
• Better Agents: The agent paradigms (like AutoGPT style) have a lot of room to become more efficient and reliable. Expect frameworks to get better at this, perhaps mixing planning algorithms with LLMs. Also, the concept of 12-factor agents (drawing analogy to 12-factor app for reliability) is floating around – principles like having deterministic evaluation mode, state management, etc., for agents to be production-grade.
• Multimodal Context: So far we focused on text. But context can include images, audio, structured data. Newer multimodal models can take in an image or tables as part of context. Future context engineering might involve, say, feeding an image’s description + the image itself to a model. Or combining vectors from text and image. This broadens what “context” means.
• Human in the loop: In critical applications, context engineering might include asking for clarification or feedback from a human at certain steps (an agent could defer to a human tool). Designing when to do that is part of the process (e.g., if the AI is not confident or a decision has legal implications, involve a human).
• Standardization: As this field matures, we might see standard design patterns or even “context engineering” job roles where people specialize in this craft. Just as prompt engineering became a term (even if we joked it’s just making good prompts), context engineering encompasses a lot of systems thinking. Companies might develop internal guidelines, like “always do X with conversation memory, here is our retrieval template, here is how to do tool integration safely,” etc.
• Model Improvements: It’s worth noting that future model architectures might handle retrieval internally (some already explore that) or be less hallucination-prone. That could change how much external scaffolding we need. But given the infinite amount of knowledge in the world versus finite model parameters, retrieval and external context likely remain vital.

Finally, remember that context engineering is a means to an end: enabling LLMs to be useful, accurate, and aligned in real applications. It’s not about fancy prompts for their own sake. It’s about building AI systems that can truly help users with the right information at the right time. In that sense, it’s a blend of software engineering, data engineering, and a bit of psychology (understanding how the AI “thinks” via prompts). It’s a new discipline and you are at the frontier of it! Use these skills responsibly and creatively.

Thank you for journeying through these 6 weeks. Go forth and build some amazing LLM-driven agents!

Summary – Key Takeaways (Week 6):

• Designing a full AI agent requires combining multiple context techniques: instructions, memory, retrieval, tools, etc. A robust system often has an orchestrator that decides which techniques to apply when (e.g. if query type = X, do Y).
• An end-to-end example (like our customer support bot) illustrates how context engineering pieces fit together in a production scenario. We must consider not just getting a correct single answer, but maintaining state, integrating with databases/APIs, and handling multi-turn interactions.
• Evaluation of LLM systems is crucial and should be multifaceted: automated tests for regression, plus human feedback. Ensure your changes actually improve things and don’t break earlier capabilities (regression testing is important due to the nondeterministic nature of LLM outputs).
• Context engineering is iterative. You will rarely get the perfect prompt pipeline on first try. Embrace a cycle of prototype → test → refine. Logging and observability are your friends in understanding how the model is behaving with the context you provide.
• Watch out for common pitfalls like context dilution (too much irrelevant info can confuse the model), and always guard against malicious or accidental prompt injection by users. Never blindly trust user input in your prompt without thinking of injection (especially if your agent has tools or can output something sensitive).
• Cost and performance considerations mean you should optimize context size (don’t include what you don’t need), possibly use smaller models when appropriate, and cache or pre-compute where possible. There is a trade-off between completeness of context and efficiency – find the sweet spot for your application.
• The field is evolving. New models with larger context windows or better built-in retrieval will emerge – keep learning and updating your approaches. Techniques like retrieval, summarization, and multi-step planning are likely here to stay, but how we implement them may change with new tools.
• The future of context engineering will likely involve more automation (agents that auto-tune their prompts or choose retrieval strategies), better memory plugins, and more modalities (images, etc.). But the core skill you’ve learned – thinking systematically about “what does the model need to succeed” – will remain invaluable.
• Always align your context engineering with user needs and ethical guidelines. Giving an AI a lot of context power also means if misused it could leak info or make harmful decisions. So design with safety in mind (principle of least privilege for tools, careful with user data, etc.).

Optional References for Deeper Learning (Week 6):

• “Context Engineering in Practice: Case Studies” – (Hypothetical resource) Find blogs or talks by companies who deployed LLM agents (OpenAI’s plugins paper, Salesforce’s AI assistant, etc.) to see how they engineered context and what issues they faced.
• Evaluation: OpenAI’s article on [GPT-4 System Card] – discusses evaluation of GPT-4 including prompt adherence, which gives insight into model limitations and alignment (which context engineering often tries to address).
• Holistic AI Agent Design – a conference talk or paper (if available by now) on designing AI copilots (e.g., Microsoft’s guidance on building Copilot for X).
• Research: “Toolformer” (Schick et al. 2023) – a paper where a model was trained to decide on tool use by itself. This is relevant to see the direction of integrating tool-use into the model’s training, potentially reducing reliance on prompt engineering for it.
• Communities: Join communities like the LangChain Discord or OpenAI community forums where practitioners share tips. Often, best practices are discussed in real-time as people build.
• And of course, keep experimenting with new frameworks (maybe check out [Semantic Kernel’s latest release notes] or [LangChain blog] for the newest features in context management).

Good luck, and happy context engineering!