Chunking Strategy for RAG Systems

Chunking Strategy for RAG Systems

Overview

Provides chunking strategies for RAG systems, vector databases, and document processing. Recommends chunk sizes, overlap percentages, and boundary detection methods; validates semantic coherence; evaluates retrieval metrics.

When to Use

Use when building or optimizing RAG systems, vector search pipelines, document chunking workflows, or performance-tuning existing systems with poor retrieval quality.

Instructions

Choose Chunking Strategy

Select based on document type and use case:

1. Fixed-Size Chunking (Level 1)

   • Use for simple documents without clear structure
   • Start with 512 tokens and 10-20% overlap
   • Adjust: 256 for factoid queries, 1024 for analytical

2. Recursive Character Chunking (Level 2)

   • Use for documents with structural boundaries
   • Hierarchical separators: paragraphs → sentences → words
   • Customize for document types (HTML, Markdown, JSON)

3. Structure-Aware Chunking (Level 3)

   • Use for structured content (Markdown, code, tables, PDFs)
   • Preserve semantic units: functions, sections, table blocks
   • Validate structure preservation post-split

4. Semantic Chunking (Level 4)

   • Use for complex documents with thematic shifts
   • Embedding-based boundary detection with 0.8 similarity threshold
   • Buffer size: 3-5 sentences

5. Advanced Methods (Level 5)

   • Late Chunking for long-context models
   • Contextual Retrieval for high-precision requirements
   • Monitor computational cost vs. retrieval gain