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