# Applying RAG Patterns

> Document chunking strategies, hybrid retrieval (semantic + keyword), reranking patterns, and citation/attribution

- Skill: `gitwalter/applying-rag-patterns` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add gitwalter/applying-rag-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gitwalter/applying-rag-patterns/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gitwalter (https://skillmd.com/u/gitwalter)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/gitwalter/applying-rag-patterns

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# 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

1. **Chunk documents** — Use recursive chunking for most docs (character, token, semantic, header-based).
2. **Set up retrieval** — Prefer hybrid search (semantic + keyword); single pass is acceptable for simple use cases.
3. **Rerank results** — Add cross-encoder or Cohere reranking for better relevance.
4. **Enable citations** — Format context with `[1]`, `[2]` markers and include source metadata.
5. **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 200` for 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.