What Is Context Engineering in AI?
As AI tools become more capable, experts are paying increasing attention to a concept called context engineering. While prompt engineering focuses on how instructions are written, context engineering focuses on the information an AI model has access to when performing a task.
In simple terms, context engineering is the process of providing an AI system with the right information at the right time. This can help AI tools generate more accurate, relevant, and useful responses.
Key takeaways
- Context is the information available to an AI model when it performs a task, including prompts, conversation history, instructions, documents, and external information.
- Context engineering involves selecting, organizing, and providing the right information to an AI model at the right time.
- Unlike prompt engineering, which focuses on the instructions given to an AI model, context engineering considers the broader set of information the model needs to complete a task.
- Context engineering is becoming increasingly important as AI systems take on more autonomous, multistep tasks and need to manage information within a limited context window.
What is context in AI?
Before understanding context engineering, it helps to understand what context means in AI.
Context is the information available to an AI model when it generates a response. Depending on the task, this can include the user’s prompt, previous messages in a conversation, instructions, examples, uploaded documents, or information retrieved from external sources.
For example, if you ask an AI chatbot to summarize a report you’ve uploaded, the report becomes part of the model’s context. If you’re continuing an existing conversation, earlier messages may also be part of the context.
The exact context available to an AI system varies by task, but it often includes some combination of the following:
Types of context
| Type of context | Example |
| User prompt | “Summarize this article in plain language.” |
| Conversation history | Earlier messages in the chat |
| Instructions | “Write for a beginner audience.” |
| Examples | A sample paragraph showing the desired style |
| Uploaded files | A PDF, spreadsheet, or image provided by the user |
| External information | Data retrieved from a database or search tool |
What is context engineering?
Context engineering is the process of selecting and organizing the information an AI model has access to so it can perform a specific task. It involves deciding what information is relevant, what can be left out, and how the information should be presented to the model.
Although people can provide context when interacting with AI tools, context engineering generally refers to how developers design AI systems to select, organize, and provide context to the model.
For example, imagine asking an AI assistant to answer a customer’s question about a product. The assistant will likely provide a more useful response if it also has access to relevant product information, company policies, and previous customer interactions. Deciding which information to provide—and which information to leave out—is an example of context engineering.
The goal is not to give an AI model as much information as possible but to give it the right context at the right time.
Prompt engineering vs context engineering
Prompt engineering and context engineering are closely related, but they focus on different aspects of how an AI model receives information.
- Prompt engineering focuses on how you write instructions for an AI model. It involves choosing and refining the wording of a prompt to guide the model toward the desired response.
- Context engineering takes a broader approach. Instead of focusing only on the instructions, it considers all the information the model needs to complete the task, such as relevant documents, previous interactions, examples, or other data.
A simple way to think about the difference is:
Prompt engineering: “Here is what I want you to do.”
Context engineering: “Here is what you need to know to do it well.”
The two approaches are complementary. A well-written prompt can tell an AI what to do, while the right context gives it the information it needs to do the task effectively.
Why is context engineering important?
AI models can only process a limited amount of information at once, known as the context window. More information doesn’t automatically mean better results. Irrelevant or excessive context can make it harder for a model to focus on what matters for the task, much as a person given a stack of loosely related documents might struggle to find the one detail they need.
When a model has to sift through too much unnecessary information, it can miss important details or produce a less focused response.
Context engineering matters more as AI systems known as AI agents take on more autonomous, multistep work. For example, an AI assistant asked to reply to a colleague’s email and suggest a meeting time might need to pull from:
- The email itself
- The user’s calendar
- Relevant information from previous conversations
As tasks get more complex, developers need to manage what information the model receives and when. Context engineering does this by deciding:
- What information the model should have access to
- How it should be organized
- When it should be provided
Done well, this keeps the model working with a focused, relevant set of information, rather than sifting through everything it could possibly have access to.
AI researcher Andrej Karpathy has compared AI models to a new kind of operating system, where the model acts as the CPU and its context window as RAM—the model’s working memory. Just like RAM, a context window has limited capacity, so context engineering is about deciding what information the model needs on hand, and when.
Frequently asked questions about context engineering
- What is a context window in AI?
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A context window is the amount of information an AI model can consider at one time when generating a response. This can include the user’s prompt, conversation history, instructions, uploaded files, and other information provided to the model.
Because a context window is limited, AI systems need to manage which information is available to the model, especially during longer or more complex tasks. This is an important part of context engineering, which focuses on providing an AI model with the right information at the right time.
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