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简体中文 | English

Context Management

The "intelligence" of your AI coding tool is directly proportional to context quality. Too little and it doesn't understand your project. Too much and it actually gets dumber. Managing context well is a key skill for effective AI-assisted development.

How Context Works

Every AI coding tool has a context window — think of it as the AI's "working memory."

Tool Context Window Notes
Claude Code 200K tokens ~150K words, largest among coding tools
Cursor 128K-200K tokens Depends on the selected model
Copilot 128K tokens Includes open files
Gemini CLI 1M-2M tokens Largest window, but bigger isn't always better

Key insight: More context is not always better. Stuffing in irrelevant information causes "attention dilution" — important details get buried.

Core Strategies

1. Feed Precisely, Don't Dump Everything

❌ Bad: "Look at the entire src/ directory and find the bug"
✅ Good: "Look at the refreshToken function in src/services/auth.ts —
    users report getting 'token expired' after refresh.
    Also check src/middleware/auth.ts to see how tokens are validated."

2. Project Config Files Are Your Most Efficient Context

Every tool has a project config file that loads automatically at startup:

Tool Config File Purpose
Claude Code CLAUDE.md Project background, conventions, common commands
Cursor .cursorrules / .cursor/rules/ Project rules
Copilot .github/copilot-instructions.md Project guidelines
Windsurf .windsurfrules Project rules
Gemini CLI GEMINI.md Project config

A well-written config file = the most critical context automatically included in every conversation.

3. Refresh Periodically During Long Conversations

The longer a conversation runs, the less weight earlier messages carry. When AI starts "forgetting" or producing lower-quality responses:

# Claude Code
> /clear    # Clear current conversation, start fresh

# Or summarize manually
> Here's what we've done so far: [summary].
  Now continue with [next task].

4. Use Files to Pass Context, Not Chat

❌ Bad: Pasting 500 lines of code into the chat for analysis
✅ Good: The code is already in a file — just tell AI where to look
    "Read the processRefund function in src/services/payment.ts, lines 100-150"

Tool-Specific Context Management

Claude Code

# Use CLAUDE.md for persistent context
# Use Memory for cross-conversation recall
# Use /clear to reset the conversation

# For large projects: use subagents to isolate context
"Use a subagent to analyze the src/payment/ code —
 don't pollute the main conversation's context."

Cursor

# Use @ references for precise context control
@src/models/user.ts @src/schemas/user.ts
Write a user registration endpoint based on these two files

# Use Notepads to save frequently used context
# Use Rules globs to auto-load rules by file type

# Open related files as implicit context
# Cursor reads your currently open tabs

Copilot

# Use #file #selection #terminal for precise references
#file:src/models/user.py Write tests based on this model

# Open related files — Copilot reads your open tabs
# Close unrelated files — reduce noise

# Agent mode auto-searches for related files
# But you can use #file to narrow scope and improve accuracy

Common Issues

Problem Cause Solution
AI gives inaccurate answers Not enough context, or too much noise Reference specific relevant files
AI gets progressively worse Conversation too long, context overflow Start a new conversation with your config file
AI forgets earlier agreements Early messages lose weight over time Put important conventions in the config file
AI invents non-existent APIs Insufficient reference material Have it grep/search to confirm before using
AI re-does work it already completed Doesn't remember prior progress Track progress with a todo/plan