How to Give AI Coding Agents Better Context

If you've used Cursor, Claude, or GitHub Copilot, you've probably noticed something: the AI is great at writing code, but it often writes the wrong code for your project.

It doesn't know your tech stack. It doesn't know your naming conventions. It doesn't know that you're building a mobile app, not a web app. So it guesses, and you spend time correcting it.

The fix is simple: give the AI context about your project before asking it to write code.

Why context matters

AI coding tools work by predicting what code comes next based on what they've seen. If all they see is a single file, they'll make assumptions based on that file alone.

But your project has structure. It has decisions you've already made. There's a database schema, an API design, a reason you picked Next.js over Remix. The AI doesn't know any of this unless you tell it.

Good context transforms AI from "helpful autocomplete" into something more like a junior developer who's read your documentation.

What to include in context files

Most AI coding tools let you reference files or provide system prompts. The question is: what should those files contain?

Here's what works:

1. Product overview

A short description of what you're building, who it's for, and what problem it solves. Two paragraphs is enough. This helps the AI understand the why, not just the how.

2. Tech stack and architecture

List your technologies: frontend framework, backend language, database, hosting. Include any important architectural decisions. For example: "We use tRPC for type-safe API calls between Next.js and our Node backend."

3. Coding conventions

If you have preferences, state them. "We use functional components only" or "API endpoints follow REST naming" or "All database queries go through repository classes." The AI will follow whatever patterns you specify.

4. Current phase or focus

What are you working on right now? If you're building the authentication system this week, say so. The AI will prioritize relevant suggestions.

Setting this up in Cursor

Cursor has a feature called "context files" where you can add markdown files to your project that the AI will reference.

Create a .cursor folder in your project root and add files like:

• product.md - What you're building
• tech-spec.md - Tech stack and architecture
• conventions.md - Code style and patterns

Reference these in your Cursor settings, and now every conversation starts with the AI knowing your project.

Setting this up in Claude

Claude's project feature lets you upload documents that persist across conversations. Upload your context files once, and Claude will reference them whenever you chat about that project.

You can also paste context at the start of a conversation if you're using the API or a one-off chat.

The lazy way: generate context automatically

Writing documentation is boring. Most developers skip it, which means they don't get the benefits of good AI context.

Here's an alternative: use a planning tool that generates these files for you.

When you ideate a project in CollabLearn, we generate:

• @product.md - Product description and user stories
• @tech-spec.md - Technical architecture and stack decisions
• @active-plan.md - Current phase and what to build next

You can push these directly to GitHub. Now when you open your project in Cursor or reference it in Claude, the AI already knows everything about your project.

The difference it makes

Here's a before and after example:

Without context:

The AI generates a generic login form. No styling, wrong framework, missing your auth provider.

With context:

The AI generates a login form that uses your design system, integrates with your auth provider (Supabase, Auth0, whatever you specified), and follows your component structure.

Same prompt, completely different output. The AI isn't smarter; it just has better information.

Start small

You don't need perfect documentation to get value from context files. Start with one file that answers:

1. What are you building? (one paragraph)
2. What's your tech stack? (a list)
3. What are you working on this week? (a sentence)

That's maybe 10 minutes of writing, and it'll save you hours of correcting AI suggestions.

Beyond static files: live sync with MCP

Context files are great, but they're static. You write them once, and they get stale as your project evolves.

There's a newer approach: MCP (Model Context Protocol). It's a way for your AI coding agent to connect directly to external tools and fetch live context.

CollabLearn has an MCP server you can connect to Cursor, VS Code, or Claude Desktop. Once connected, your AI agent can:

• Fetch your current roadmap and tasks in real time
• See which phase you're working on right now
• Read your product specs and tech architecture
• Mark tasks as done when it completes them

The context is always fresh because it's pulled from your actual project state, not a file you wrote last week.

If you're using Cursor or Claude Desktop regularly, it's worth setting up. Go to /docs/mcp in CollabLearn to get your API key and config.

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CollabLearn generates project context files automatically when you plan your project. For live sync, connect your AI agent via MCP.