Advanced Context Management: Handling Large Repositories

• Jader Correa

Advanced Context Management: Handling Large Repositories

One of the biggest challenges in AI coding is the Context Window.

How Chuchu Manages Context

Chuchu uses Retrieval-Augmented Generation (RAG)1 to fetch only relevant information:

  1. Project Map: The project_map tool generates a tree-like view of your project structure in ~500 tokens, giving the model a “mental map” of where things are.

  2. Semantic Search: When you ask a question, agents use:
    • search_code: grep-based pattern matching to find relevant code
    • list_files: discover files matching patterns (e.g., *.go, test_*.py)
    • read_file: read specific files (with automatic truncation for large files)
  3. Smart Retrieval: Instead of dumping your entire codebase into context, agents:
    • Ask specific questions → retrieve only relevant snippets
    • Read file summaries before full content
    • Truncate large results automatically (first 200 lines of files, first 30 files in listings)

Tips for Large Repos

If you are working in a massive monorepo, here are some tips to help Chuchu stay focused:

1. Chuchu Respects .gitignore

Chuchu automatically respects your .gitignore and skips common directories:

# Automatically ignored:
node_modules/
vendor/
target/
dist/
build/
.git/
__pycache__/
.venv/
.idea/
.vscode/

No extra configuration needed - it just works!

2. Be Specific in Prompts

Instead of “Fix the bug in the auth system”, try:

“Fix the nil pointer in auth/login.go when the user ID is empty. Check auth/types.go for the struct definition.”

This guides the agent to read exactly what it needs, saving tokens and improving accuracy.

3. Start Fresh Sessions

If a conversation gets too long, the context can get “polluted” with old information. There are several ways to start fresh:

In Neovim: Close the chat buffer with Ctrl+D and reopen with <leader>cc to start a new session.

CLI: Exit the current chu chat session (Ctrl+D) and start a new one.

Better approach: Use command-based workflow to avoid long sessions:

# Instead of long chat sessions, use focused commands:
chu research "how does the auth system work"
chu plan "add OAuth support"
chu implement plan.md

Each command starts with fresh context, preventing pollution.

What Makes This Effective

Chuchu already uses RAG! The combination of:

  • project_map for structure overview
  • search_code for pattern-based retrieval
  • read_file with smart truncation
  • Specialized agents that know what to fetch

…means agents retrieve only what’s needed, when it’s needed. No bloated context, no wasted tokens.

Future Enhancements

Vector embeddings: We’re exploring semantic search using vector embeddings (“Find code that handles user logout” without knowing exact function names). This would complement the existing grep-based search for even better retrieval accuracy.

Codebase indexing: Pre-indexing repositories for faster symbol lookup and cross-reference navigation.

Adaptive context: Dynamic context window management based on task complexity and available token budget.

References

  1. Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS 2020. https://arxiv.org/abs/2005.11401 ↩