CONTEXT ENGINEERING Explained With Examples

Key Takeaways

I love this because it really captures what context engineering is. The model is only as good as the context you give it. Think of prompt [music] engineering as writing a sentence. Context engineering is designing the entire environment around that sentence. Context engineering is definitely worth exploring. [music] Hello everyone, my name is Don Kalawarachi. I'm a software product and platform engineering specialist focusing on all things AI. In this tutorial, we're going to explore a concept that is becoming essential for building reliable and trustworthy AI systems, context engineering. So, let's get started. If you're just starting out in AI or data science, don't worry. You don't need to be an expert to follow along. Think of this as a guided introduction. We will unpack what context means for AI, why it matters, and how you can work effectively with it. Before we go any deeper, let's clarify what we mean by context. Especially in the case of language models, tools like GPT5 or Clude 4 don't actually retain memory between messages. What they generate depends entirely on what is inside the context window. That window includes your prompt, system instructions, retrieve data, and many more. If the right context isn't there, the output won't be reliable. Which is why we say the model is only as good as the context you give it. Large language models can actually do amazing things, but in practice, they often break down when tasks are too complicated. For example, they may forget earlier parts of conversations. They may generate information that sounds plausible but is not accurate. They may not understand what the user actually wants and produce a wrong output. When these occur, it is a sign that there is an issue with the structure in which we feed the model information. So what is context engineering? It is essentially the art of managing what information a model sees before it responds. This includes memory, retrieval, system rules, tool metadata, and more. When we structure the context well, the output becomes consistent and accurate. Context engineering is also described as the art of filling the context window with just the right information for the next step. I love this because it really captures what context engineering is. It's about carefully managing everything that the model sees before it generates a response rather than just being about clever prompts. It might also be useful to think about context engineering as including the prompt, system instructions, retrieve documents, conversation history, and tool data. The term context engineering took off in 2025, but the work actually started much earlier. In 2023, developers were already using retrieval augmented generation or RAG to pull in documents. In 2024, memory systems and tool orchestration became common in agent workflows. By 2025, the industry started calling the whole process which includes retrieval, memory, prompts, tool metadata, one discipline. In other words, they started calling it context engineering. As language models evolved, so did the inputs we give it. We started off with simple prompts, then added system instructions for behavior. Afterwards, it was memory for state tracking, then retrieval for grounding knowledge and tools and APIs for structured executions. Each of these layers made the model more capable, but also more dependent on having the right context. This is why we need to make sure that we have the right context and why context engineering is really important. When we think about interacting with AI models, we usually just think about prompts. That is what we type into the chat box. But prompts are only one part of it. The model is also shaped by system instructions, memory, retrieved knowledge, tool information, and personal preferences. All of these work together to create effective contextual understanding. Prompt engineering is essentially one part of a bigger puzzle which is context engineering. Because many are usually confused about the difference between prompt engineering and context engineering. Let's look at what prompt engineering is. Prompt engineering is about crafting the user's question. It focuses on wording, tone, and short examples. Context engineering is much broader. It includes additional signals that the model uses. For instance, system prompts, memory, tools, user data, and environment. It is what powers long-term understanding and consistency. Think of prompt engineering as writing a sentence. Context engineering is designing the entire environment around that sentence. So what makes up context? It starts with system prompts. These are invisible instructions that guide the model's behavior and tone. Next is conversation state. This is the dialogue history or the memory of what has been said so far. Then there are user preferences. These are things like your language preferences or your technical depth. Retrieved knowledge brings in documents or facts through systems like rag. Tool metadata tells the model how to use external tools and APIs. Output schemas guide the response format, especially for structured answers like JSON or forms. Together with prompts, these elements create a full context window. Here is how it all fits in together. The model does not just read your message. It reads the whole input window. This can include your prompt, system instructions, memory, retrieve documents, tool info, and output format. Think of it as a densely packed message full of context clues. The better we organize it, the better the model perform, the better the response we get. You might wonder, why not just give the model everything? Isn't it better to always give the model more context? But that does not actually always work. While large context windows are impressive and could help with the outputs that are produced, performance often drops at about 35,000 tokens, i.e. when the context is bigger, the performance might actually drop. The model actually struggles to attend to everything. So important details can get lost in the noise. It is not just about size. It is more about signal. A large cluttered context window can confuse the model. A curated one i.e. one that is focused and relevant helps the model reason better, make fewer mistakes and stay aligned to users goals. In production systems, context is very powerful. But it can also be quite tricky. There are four common failures that we need to watch out for. Context poisoning, context distraction, context confusion, and context clash. We will look at each of these in detail, what it looks like, why it happens, and how to prevent it. First, let's start off with context poisoning. This happens when force or inaccurate information enters the model. It might come from user messages, bad retrievalss, or corrupted memory. Because the model treats context as trustworthy, a single poison block can distort many future responses. You can defend against these with validation layers. Check provenence, whit list sources, source content, and quarantine suspicious documents to reduce context poisoning from occurring. Next, context distraction. This happens when a model is fed too much or the wrong kind of history. Even true details can be distracting and irrelevant to the task. The fix is to compress and summarize long histories. Keep recent and task relevant details. Use retrieval to pull only the most useful snippets and this will help reduce context distraction. Third, we have context confusion. This arises when signals are vague, missing or poorly structured. The model might call the wrong tool, misuse an API, or misinterpret retrieved documents. The solution is to make tool metadata explicit. Use retriever to pre-seelelect the tools or documents. provides schemas and confidence scores so that models can choose correctly. This will help reduce context confusion. Finally, we have context clash. This happens when the context window contains conflicting information. Two sources that say opposite things. The model then guesses and gives inconsistent answers. The remedy is reconciliation. Choose true sources. Apply conflict resolution rules and prune stale state. The context must always represent one consistent view. Here's a quick summary. Context poisoning is bad inputs contaminating outputs. Fix it with validation checks. Context distraction is noise that overwhelms the model. Fix it with summarization and prioritized retrievalss. Context confusion is vague inputs that lead to the wrong actions. Fix it with explicit metadata and smarter selection. Context clash is contradictions that break consistency. Fix it with conflict resolution and true sources. Use this checklist when designing or auditing your systems. If you want to dive deeper, here are some sources. Deep Mind for Gemini, OpenAI for models and engineering posts, datab bricks for retrieval and LLM ops. Always make sure you use official documentation and brand assets for up-to-ate guidance. So where is context engineering used today? There are four high impact areas. Customer service bots that pull account information before replying. document Q&A tools that retrieve the right information, AI coding assistants that read your repo, and multi- aent planners that share memory across roles. These are systems you already use powered by structured context. Let's look at all of them in a bit more detail. Let's start with customer service bots before replying to you. They actually retrieve account data, pass ticket summaries, and look at troubleshooting guides. That way, when you ask them about billing issue, the response speaks to your account, your history, and your needs. Context makes the bot feel personal and relevant. Next, we have document Q&A tools, which depend on retrieval. They split large copra into chunks, embed them and pull back only the most relevant parts when you ask a question. This keeps your answers grounded in the source text, reduce hallucinations and builds trust. Next, we have AI coding assistance. They scan your entire repository, see open files, recall last comments, and suggest code that matches your project's style and architecture. They are context engineers, not just autocomplete machines. Multi-agent planners split tasks across specialized roles, a planner, a researcher, an executor. They coordinate by sharing structured context, the plan, the evidence, and the constraints. Across all of these cases, the pattern is the same. Structured input, integration with the right tools, context of ad responses, different tasks, same foundation. That is effective context engineering. So how is it done? Here are the core techniques. retrieval, augmented generation, vector databases, summarization pipelines, tool selection with metadata, scratch pads, and context of loading and agent frameworks. Think of these as your toolbox. You will need to use them when engineering your context. Rag combines search and generation. It splits into chunks, embeds them, retrieves the top matches, and feeds them into the LLM. So instead of guessing, the model works with the facts. Vector databases store embeddings for fast similarity search. They scale to millions or billions of entries. Popular tools include Pine Cone. This is where your chunks live, ready to be retrieved whenever needed. Summarization pipelines help control context size. These condense long conversations into concise summaries. They preserve key facts, decisions, and questions and update them as the context grows. Tool selection via metadata helps the model pick the right tool. Each tool has metadata that is its purpose, input types, output types. The system retrieves the best match for your task. This reduces mistakes like calling the wrong API. Scratch pads give the model a temporary workspace. They store immediate reasoning, calculations, or planning steps. They are cleared once the task is complete. Think of these as sticky notes for AI models. Agent frameworks organize multi- aent workflows. They define roles, automate context passing, and manage shared memory. They let you scale complex setups without reinventing the wheel. Each of these techniques solve a failure mode. Summarization and scratch pads reduce distraction. Rag and vector databases prevent loss of knowledge. Metadata and frameworks reduce confusion. So you have all of these tools and you can use the right tool for the right fix. If you think to yourself, should I use context engineering? Ask yourself three questions. Do I need memory across terms? Do I need to use tools or APIs? Are my tasks more complex than simple oneshot queries? If the answer is yes, context engineering is definitely worth exploring. But not every problem needs it. For simple Q&A, oneshot summarizations, or single turn commands, plain prompting just works fine. Keep it simple when you can. Less complexity means faster results. Here are the core insights. It is not just what you ask. It is what the model knows and when you ask it. Manage what information the model has access to. Structure inputs thoughtfully. Minimize noise and confusion and that will help you make a good system. So some of the key takeaways are manage input context carefully, avoid common failure modes, use tools and pipelines to structure inputs. Start small but design with structure and scale in mind. Build with intention, not just curiosity. Thank you for joining this tutorial on context engineering. So try out contextaware workflows and share your strategies with the community. If you'd like to learn more, explore more tutorials on data camp. Thank you. [music] >> [music]