Mastering Context: How to Feed Your AI Agent

You prompt: "Fix the login bug." The AI says: "I don't see a login file." You sigh.

The difference between a "Junior" AI result and a "Senior" AI result is almost always Context.

AI models like Claude and GPT-4 have massive context windows (200k+ tokens), but filling them with garbage leads to garbage results. Here is how to curate context like a pro.

The "Onion" Strategy of Context

Think of your codebase as an onion. When asking for a change, you need layers.

Layer 1: The Target (Must Have)

The file you strictly want to edit.

e.g. src/auth/LoginComponent.tsx

Layer 2: The Direct Dependencies (Should Have)

The data structures and utilities the target uses.

e.g. src/types/auth.ts (Interfaces)
e.g. src/utils/api-client.ts (How you fetch data)

Without Layer 2, the AI hallucinates types and functions that don't exist.

Layer 3: The Patterns (Nice to Have)

A similar file that is already working correctly.

e.g. src/auth/RegisterComponent.tsx

This is the secret weapon. By showing the AI a "correct" example from your own codebase, it instantly mimics your style, naming conventions, and error handling patterns.

Managing Context in Cursor

Cursor makes this easy with @symbols.

Don't just add folders. Adding @src is lazy and fills the context with noise.
Use @Codebase sparingly. It searches reasonably well, but manually adding the 3-4 exact files is always 10x better.
The .cursorrules file. This is your "Permanent Context". Put your global rules here (stack, style guide) so you don't have to repeat them.

The "Context Dump" Technique

Before starting a big refactor, create a temporary file called CONTEXT.md. Paste relevant snippets, database schemas, and business logic requirements into it. Add @CONTEXT.md to your chat.

This gives the AI a "brain" to refer to that is cleaner than reading 50 raw code files.

Summary

Be surgical: Add specific files, not whole folders.
Show examples: Always provide a "reference" file.
Use .cursorrules: For improved baseline performance.

Better input = Better output.

Check out our Context Optimization Rules →