Codebase Q&A
Answer developer questions about large codebases with context-aware AI
Enable developers to ask natural language questions about a codebase and get accurate, context-aware answers. Requires strong code understanding, long context for large repos, and precise instruction following.
The job to be done
Process developer questions alongside relevant code snippets, documentation, and repo structure. Generate accurate answers that reference specific files, functions, and patterns.
Key tradeoffs
Context window is critical — larger codebases need models with 100K+ token contexts. Reasoning quality directly affects answer accuracy. Speed matters for developer experience but can be traded for correctness.
When to switch models
Use a frontier model with large context for complex architectural questions. Switch to a faster mid-tier model for simple lookups and syntax questions. Consider open-weight models if code must stay on-premise.
Recommended models
Related guides
Frequently asked questions
How much code context should I include?
Include the most relevant files (not the entire repo). Use a retrieval layer to find the top 5-10 relevant files, then pass those as context along with the question.
Should I use function calling?
Yes. Function calling enables the model to request specific files or search for patterns, making it more effective for large codebases.
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