# RAG Chunking Strategy

> Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

- Skill: `a5c-ai/rag-chunking-strategy` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/rag-chunking-strategy`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/rag-chunking-strategy/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/rag-chunking-strategy

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# RAG Chunking Strategy Skill

## Capabilities

- Implement multiple document chunking strategies
- Configure semantic chunking based on content boundaries
- Set up recursive character text splitting
- Design fixed-size chunking with overlap
- Implement document-aware chunking (markdown, code, etc.)
- Optimize chunk sizes for retrieval quality

## Target Processes

- rag-pipeline-implementation
- chunking-strategy-design

## Implementation Details

### Chunking Strategies

1. **RecursiveCharacterTextSplitter**: Hierarchical splitting with separators
2. **SemanticChunker**: Embedding-based semantic boundaries
3. **TokenTextSplitter**: Token-aware splitting
4. **MarkdownHeaderTextSplitter**: Structure-aware markdown splitting
5. **CodeSplitter**: Language-aware code chunking

### Configuration Options

- Chunk size (characters or tokens)
- Chunk overlap percentage
- Separator hierarchy
- Embedding model for semantic chunking
- Document type detection

### Best Practices

- Match chunk size to embedding model limits
- Use appropriate overlap for context preservation
- Test retrieval quality with different strategies
- Consider document structure in strategy selection

### Dependencies

- langchain-text-splitters
- sentence-transformers (for semantic chunking)