gitwalter Rag Patterns
Document chunking strategies, hybrid retrieval (semantic + keyword), reranking patterns, and citation/attribution
Implement Retrieval-Augmented Generation with effective chunking, hybrid retrieval, reranking, and proper citation.
Process
- Chunk documents — Use recursive chunking for most docs (character, token, semantic, header-based).
- Set up retrieval — Prefer hybrid search (semantic + keyword); single pass is acceptable for simple use cases.
- Rerank results — Add cross-encoder or Cohere reranking for better relevance.
- Enable citations — Format context with
[1],[2]markers and include source metadata. - Wire pipeline — Connect chunker → embeddings → retriever → reranker → prompt with context.
Chunking Strategies Comparison
| Strategy | Best For | Pros | Cons | |-|-||| | Character-based | Simple text | Fast, predictable | Ignores structure | | Recursive | General documents | Respects structure | May split sentences | | Token-based | LLM context | Accurate sizing | Requires tokenizer | | Semantic | Related content | Groups concepts | Slower, needs embeddings | | Header-based | Markdown | Preserves hierarchy | Markdown only |
Best Practices
- Use recursive chunking for most documents
- Overlap chunks by 10–20% to preserve context
- Chunk size: 500–1000 chars for general use; adjust for model context
- Use semantic chunking for conceptual grouping
- Combine multiple retrieval methods (hybrid search)
- Rerank results for better relevance
- Always include source citations
- Store metadata (source, page, timestamp) with chunks
Anti-Patterns
| Anti-Pattern | Fix |
|---|---|
| No chunk overlap | Use 10–20% overlap |
| Too large chunks | Keep under model context limit |
| Ignoring document structure | Use header-aware chunking |
| Single retrieval method | Use hybrid search |
| No reranking | Add reranking step |
| Missing citations | Always include source metadata |
| No metadata preservation | Pass metadata through pipeline |
Resources
- TROUBLESHOOTING.md — Common issues (poor retrieval, slow indexing, hallucination, OOM, empty results)
- scripts/verify.py — Run
python scripts/verify.py --project-dir .to check RAG best practices - scripts/analyze.py — Run
python scripts/analyze.py --file doc.txt --chunk-size 1000 --overlap 200for chunk analysis - examples/basic_retriever/ — Minimal RAG with ChromaDB
- Related:
retrieving-advanced,knowledge-graphs,memory-management
When to Use
This skill should be used when strict adherence to the defined process is required.
Prerequisites
- Basic understanding of the agent factory context.
- Access to the necessary tools and resources.