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Building Context Files

Building Context Files

A context file is more than a reference document, it’s the foundation that shapes how an AI understands and interacts with your environment.

When built well, a context file transforms a general-purpose model into a domain-aware collaborator that can answer questions, perform tasks, and reason within your rules.
In this section, you’ll learn how to build a context file from scratch, step by step, using an example API as a running illustration.

Example Context Scenario

Let’s imagine you’re working with an AI that helps developers write, test, and document your company’s Customer API — a simple service that manages users, subscriptions, and billing.
Your goal is to create a context file that enables the AI to understand how this API works and how to extend it safely.

1. Define the Goal and Audience

Before writing anything, get clear on why this context file exists and who it’s for.

Ask yourself:

  • What do I want the AI to do with this information?

  • Who or what will consume this file — a developer, an AI code assistant, or a chatbot?

  • What does “success” look like when this file is used?

For the Customer API, the goal might be:

“Enable an AI assistant to generate and update REST API documentation, example requests, and endpoint descriptions that accurately match our implementation.”

This simple definition gives your context a north star. Everything you include should support that outcome.

2. Gather Source Material

Now, collect and identify the truth sources the AI should reflect, the real artifacts that define how your system works.

For the Customer API, you might gather:

  • README.md (project overview)

  • /routes/customer.js (endpoint definitions)

  • /docs/openapi.yaml (schema and example requests)

  • A short internal doc explaining authentication and versioning rules

As you collect material, ask:

  • Is it accurate (current, verified)?

  • Is it relevant to the task?

  • Is it readable by the model (plain text or easily summarized)?

You’re not copying these sources into the context; you’re identifying and preparing to distill them.

3. Extract Core Knowledge

Your next step is to identify what the AI must understand to perform effectively.
Think of this as boiling your sources down to their essential knowledge.

For our Customer API, the extracted points might look like this:

  • Base URL: /api/v1/customers

  • Supported methods: GET, POST, PUT, DELETE

  • Authentication: required via Bearer token

  • Response format: JSON with consistent { success, data, error } shape

  • Error handling: standardized error codes in /utils/errors.js

  • Versioning: major changes tracked in CHANGELOG.md

You’re capturing the core truths that define the system’s behavior — not every detail, just what’s required for correct reasoning and output.

4. Organize and Structure

Now you’ll shape those ideas into a hierarchy that the AI (and humans) can follow.
Start broad and move toward detail, just as you would in a good technical document.

Here’s an early version of your context file structure:

# Customer API Overview The Customer API manages users, subscriptions, and billing data. ## Endpoints - `GET /api/v1/customers` – Fetch all customers - `POST /api/v1/customers` – Create a new customer - `PUT /api/v1/customers/:id` – Update a customer - `DELETE /api/v1/customers/:id` – Remove a customer ## Authentication All routes require a `Bearer` token in the `Authorization` header. ## Response Format Every response includes `{ success, data, error }`. ## Error Handling Error codes are defined in `/utils/errors.js` and should follow standard format.

At this point, your context file is structured, but not yet refined — it’s readable, hierarchical, and aligned with the system’s real behavior.

5. Write with Precision

Now you’ll turn structure into refined text that maximizes meaning per token.
Use short, declarative sentences, consistent terminology, and examples that illustrate key rules.

Here’s how the Customer API file evolves after tightening the language and adding clarity:

# Customer API Overview The Customer API provides CRUD operations for managing customers and their subscriptions. ## Endpoints - `GET /api/v1/customers`: Returns an array of all customers. - `POST /api/v1/customers`: Creates a new customer record. - `PUT /api/v1/customers/:id`: Updates an existing customer by ID. - `DELETE /api/v1/customers/:id`: Deletes a customer by ID. ## Authentication All routes require `Authorization: Bearer <token>`. Tokens are validated by the middleware in `/src/middleware/auth.js`. ## Responses Responses follow this structure: { "success": true, "data": {}, "error": null } ## Error Handling Error codes are centralized in `/utils/errors.js` and must be used consistently across routes. _Last updated: Oct 2025_

Now your file is precise, readable, and consistent — each line has purpose.

6. Test and Iterate

No context is complete until it’s tested.
Use your file with an AI and observe how well it performs.

For example:

  • Ask the AI to document a new endpoint: PATCH /api/v1/customers/:id/status

  • Does it follow the same naming and structure conventions?

  • Does it include authentication and response formatting correctly?

If it makes mistakes, identify the cause:

  • Missing information? → Add it.

  • Outdated rule? → Correct it.

  • Ambiguous instruction? → Clarify it.

Keep a short changelog in the file or your workspace:

**Changelog** - Added response format section (AI was omitting "error" field) - Clarified authentication header syntax

Iteration turns your context from a static reference into a feedback loop.

Using AI to Automate Context Creation

You don’t have to build every context file from scratch; AI can be a powerful partner in the process.

Modern models can scan a codebase, documentation, or dataset and generate an initial draft that captures structure, key components, and relationships. This gives you a strong starting point that you can refine and verify.

For example, with a codebase like the Customer API, you could:

  1. Ask an AI to summarize your routes, controllers, and data models.

  2. Have it generate a first-pass context file (api-overview.md or copilot-instructions.md).

  3. Review the output for accuracy, fill in missing details, and remove noise.

  4. Feed the refined file back into the model to re-test its understanding.

This approach automates the boilerplate — the repetitive parts of context building — while keeping human judgment at the center.
The AI accelerates information gathering and structuring, but you still decide what’s accurate, relevant, and worth including.

Tip: Treat AI as a context engineer’s assistant, not an author.
It drafts; you curate. The best results come from alternating between generation and refinement.

Conclusion

Building a context file is an engineering process, not a writing exercise.
You start by defining intent, extract only what matters, structure it clearly, test it rigorously, and keep it alive as your system grows.

A well-built context file doesn’t just describe your system — it teaches the AI how to think within it.