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Handle code-aware retrieval by preserving symbols, file structure, and API context.
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Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
Basé sur la classification professionnelle SOC
| name | rag-for-code-documentation |
| title | RAG for Code Documentation |
| description | Handle code-aware retrieval by preserving symbols, file structure, and API context. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
| category | data-type-handling |
| tags | ["code","programming","syntax","api","documentation"] |
RAG for code documentation requires specialized handling due to code's structured nature, syntax-specific patterns, and the importance of preserving function signatures, imports, and contextual relationships. This skill covers embedding code snippets, handling API references, and retrieving code-aware context.
Generic RAG approaches struggle with code-related queries:
Parse code files into structured chunks that preserve syntactic units.
Why: AST-based parsing preserves code structure that would be lost with generic text chunking.
Use embeddings designed for code or augment text embeddings with code-aware features.
Why: Code-specific embeddings capture semantic meaning in code (function relationships, patterns) that general embeddings miss.
Store code chunks with rich metadata for effective retrieval.
Why: Rich metadata enables filtering by language, type, file path, and other code-specific attributes.
Search with code-specific considerations.
Why: Code-aware search combines semantic understanding with precise filtering for programming-specific use cases.
Specialized handling for API reference queries.
Why: API queries often require exact matching (function/class names) rather than pure semantic search.
Practical references include the CodeBERT paper, GraphCodeBERT paper, StarCoder paper, and GitHub code search.
