Documenting Legacy Code with LLMs
Approach 1: A Streamlined Approach
Challenge:
Legacy codebases often exceed LLM context windows, making comprehensive documentation difficult.
Solution:
- Create an Index – Outline key areas such as: Setup, File Structure, Business Logic, Database, Framework.
- On-Demand Generation – Generate documentation sections only when requested by users.
- Improve AI Understanding – Use vector embeddings of the codebase and implement Retrieval-Augmented Generation (RAG).
Role of Git History and LLM Access:
- Version Tracking: Git history provides context for code changes, aiding in documentation.
- Code Evolution: Helps LLMs understand why and how the codebase has evolved.
- Enhanced Retrieval: Improves accuracy when combined with RAG-based documentation.
Approach 2: AI-Assisted Developer Communication
Concept:
Similar to how human developers understand code by discussing it with teammates, an AI system can learn through conversations with developers.
Implementation:
- Integration with Chatbots & Voice Assistants – Connect AI to project developers for real-time code understanding.
- Contextual Understanding – The AI system listens to and analyzes discussions in team meetings and chat groups.
- Adaptive Learning – The AI continuously refines its knowledge based on discussions and code changes.
This approach enables a dynamic and human-like understanding of legacy code while ensuring accurate and up-to-date documentation.
Approach 3: Code Visualization & Flow Mapping
Concept:
Visualizing code flow and relationships can help developers understand complex systems.