repository understanding

Repository understanding enables AI to analyze entire codebases, comprehending architecture and dependencies. **Why it matters**: Real coding tasks require understanding how files interact, not just single-file context. Large codebases exceed context windows. **Approaches**: **Indexing**: Parse and index all files, retrieve relevant context for queries. **Embeddings**: Embed code chunks, retrieve semantically similar code. **Graph construction**: Build dependency graphs, call graphs, inheritance hierarchies. **Summarization**: Generate summaries per file/directory, hierarchical understanding. **Key capabilities**: Answer questions about codebase, navigate to relevant code, understand system design, identify dependencies, trace data flow. **Tools**: Sourcegraph Cody, Cursor codebase chat, GitHub Copilot Workspace, Continue, custom RAG systems. **Technical challenges**: Keeping index updated, handling massive repos, choosing retrieval scope, context window limits. **Implementation patterns**: Hybrid of symbol indexing + semantic search + LLM reasoning. **Use cases**: Onboarding new developers, impact analysis for changes, architectural understanding, finding similar code. Essential capability for truly intelligent coding assistants.