chunking-strategy

name	chunking-strategy
description	Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
allowed-tools	Read, Write, Bash

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