If you talk to a modern language model, what matters even more than the model itself is what you feed it. Garbage in, garbage out. You've probably heard of "prompt engineering," the art of writing the perfect question to get the best answer from generative AI tools like ChatGPT, Gemini, Claude, and others. For a while, prompt engineering has been the most popular approach to achieving the best output and unlocking the full potential of AI.
But what if I told you there's a new, more powerful skill emerging, one that's less about asking the right questions and more about creating the right environment for the AI to think? This new frontier is called "context engineering," and it's the secret ingredient that's transforming AI from a clever chatbot into a truly intelligent partner.
From Prompts to Context: A New Era of AI
By default, large language models (LLMs) don't "know" anything. They predict text based on the context window you hand them, and if that window is stuffed with stale chat history, random API responses, or half‑formatted code, even the world's best model will struggle. However, on the other hand, a lean bundle of instructions, examples, and up-to-date facts can make a smaller, cheaper model stand out; that is, context engineering.
Why is this such a game-changer?
- LLMs can't read minds. They respond only to what they "see."
- The right context means better, safer, more relevant results, every time.
- As AI tools become more integrated in our daily work, "context" is now table stakes for reliability and trust.
What is Context Engineering?
So, what exactly is context engineering? To put it in the right words, context engineering is the practice of strategically organizing everything an AI needs (information, tools, background details, and history) to allow the AI model to perform a task just like a human would.
Think of an AI's "context window" as its short-term memory. Prompt engineering is about what you write in that window. Context engineering, on the other hand, is about everything else you can put in there to help the AI succeed. It's the difference between asking a stranger for guidance (prompt engineering) and giving them a map, a compass, and the address of your destination (context engineering).
This broader approach is becoming increasingly important as AI systems, often called "autonomous AI agents," are asked to perform more complex, multi-step tasks. These agents need more than just a single instruction; they need a rich, dynamic environment of information to draw from. Most of the time, when an AI agent fails, it's not because the AI model itself isn't smart enough, but because it was missing a crucial piece of information, a "context failure."
How is it different from prompt engineering?
Think of prompt engineering as writing the last mile of instructions (tone, persona, format). Context engineering is the supply-chain management that ensures those instructions sit on top of accurate and relevant data. In many applications, the context itself dwarfs the literal prompt. That's why LangChain's engineers argue prompt writing is now "just one tool in the context engineer's kit."
Here are some of the key elements that make up an AI's context:
- Instructions and System Prompts: These are the foundational rules and guidelines that tell the AI how to behave. This is where you can set the AI's persona, its goals, and any constraints it needs to follow.
- User Input: This is the immediate question or task you give the AI.
- Short-Term Memory: This includes the history of your current conversation, allowing the AI to remember what you've already discussed and build upon it.
- Long-Term Memory: This is a persistent knowledge base that the AI can access across multiple conversations. It can include your preferences, past projects, or any other information you've told it to remember.
- Retrieved Information (RAG): This is where things get really powerful. Retrieval-Augmented Generation (RAG) allows AI to pull in external, up-to-date information from documents, databases, or the internet to answer your questions.
- Tools: You can give an AI access to "tools," which are essentially functions it can call to perform specific actions, like checking your calendar, sending an email, or searching the web.
- Structured Output: You can also provide the AI with a specific format for its response, like a JSON object, which is incredibly useful for making AI-powered applications more reliable.
From "just write a clever prompt" to a repeatable process
Early adopters treated prompt writing like haiku—short, mysterious, and occasionally brilliant. As apps improved and became multi-step agents, that broke down. Andrej Karpathy nailed the change in a recent tweet: "Prompt engineering is what you do inside the window. Context engineering is how you decide what fills the window."
Today, best‑practice teams run context engineering as a pipeline:
- Collect user input, tool outputs, database records, and relevant documents.
- Select the smallest subset that actually helps.
- Transform it into a format the model understands, often JSON or structured markdown.
- Evaluate the result using both automated tests and human review, then refine the loop.
Why Context Engineering Is Replacing Prompt Engineering
Business and technical leaders are moving away from one-off quick fixes to scalable, systematic solutions. Here's how context engineering stands apart:
- Prompt Engineering:
Prompt engineering focuses on writing better questions or commands, relying on trial and error, and is most effective for simple, single-shot requests.
- Context Engineering:
Context engineering focuses on providing the right knowledge, memory, and tools. It allows continuous, personalized, and multi-step interactions. Context engineering supports more complex use cases: customer support, digital assistants, automated research, enterprise chatbots, and more.
How Context Engineering Works in Practice
- Write: Save important details outside the main prompt, ready to be used as needed.
- Select: Pull the most relevant information into the conversation at the right moment.
- Compress: Process large data sets into summaries that fit within the AI's "working memory."
- Isolate: Organize bits of information so they can be referenced without confusion.
Real‑world scenarios you already know
From Clunky to "Magical": Why Context Engineering Matters
The difference between an AI with good context and one without is the difference between a clunky, unhelpful tool and a "magical" assistant that seems to anticipate your needs. Imagine you ask an AI to schedule a meeting for "tomorrow." A basic AI, with no context, might respond with a generic, "What time works for you?"
But an AI powered by rich context would be a different story. Before responding, it would have access to your calendar, see that you're fully booked, and know from your past emails with this person that you have an informal relationship. It might even have a tool to send a calendar invite. With all this information, it could generate a much more helpful response, like: "Hey Jim! Tomorrow's packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works."
This is the power of context engineering. It's not about a smarter AI model; it's about providing the right information, in the right format, at the right time.
In Conclusion:
If prompt engineering taught us to speak to machines, context engineering teaches us to set the conversation's stage. It's less glamorous than inventing new neural nets, but it's where real‑world AI succeeds. Context engineering flips the script on "prompt magic." It's not about tricking an AI with clever phrasing; it's about building a smarter environment so the AI helps you solve real problems reliably.
Context engineering recognizes that the key to opening an AI's true potential lies not just in the questions we ask, but in the rich, dynamic environment of information we provide. We can turn an AI from a mere "chatbot" into a truly intelligent and autonomous agent by thoughtfully curating an AI's context, i.e., core instructions and memory, the tools and data it can access. Because in the era of smart automation, it's not only what AI can do—it's what you give it to work with, and how you set the stage for success.
Sources:
https://www.promptingguide.ai/guides/context-engineering-guide
https://rlancemartin.github.io/2025/06/23/context_engineering/
https://x.com/karpathy/status/1937902205765607626
https://www.philschmid.de/context-engineering
https://simple.ai/p/the-skill-thats-replacing-prompt-engineering
https://github.com/humanlayer/12-factor-agents
https://blog.langchain.com/the-rise-of-context-engineering/
