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chunking-strategy

Identify optimal chunk boundaries for retrieval accuracy across markdown, code, PDF, and plain text documents.

Updated May 25, 2026
Search Tags:#rag#retrieval#chunking#document-processing#vector-database
One-click install
npx skills add https://github.com/yehezkieldio/formalist --skill chunking-strategy-yehezkieldio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chunking-strategy
Source: https://github.com/yehezkieldio/formalist/tree/main/.agents/skills/chunking-strategy
Command: npx skills add https://github.com/yehezkieldio/formalist --skill chunking-strategy-yehezkieldio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a structured approach to designing and evaluating chunking strategies for retrieval-augmented generation (RAG) systems, enabling reliable chunk boundaries, coherent context, and scalable processing of large documents.

Core Features & Use Cases

  • Recommends chunk sizes (256-1024 tokens) and overlaps (10-20%) to balance context and retrieval precision.
  • Offers semantic boundary detection and structure-aware chunking for mixed content (markdown, code, PDFs).
  • Includes evaluation guidance with retrieval precision/recall metrics and coherence checks for production-grade pipelines.
  • Use cases include building vector-search pipelines, processing lengthy documents, and optimizing RAG workflows in enterprise data environments.

Quick Start

Experiment with a document by selecting a chunking strategy, set chunk_size and overlap, run chunking, and evaluate retrieval quality.

Frequently Asked Questions about chunking-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to chunk documents for RAG retrieval?▼

Optimal RAG chunking balances context and precision by producing 256-1024 token chunks with 10-20% overlap. Semantic boundary detection preserves coherence across structured markdown, code, and unstructured PDFs.

How do I set chunk size and overlap for vector database indexing?▼

Configure chunk size between 256-1024 tokens and overlap at 10-20% to maximize retrieval accuracy. Structure-aware chunking exposes metadata and evaluation hooks to tune these parameters for vector database pipelines.

Does semantic chunking work with mixed content like markdown and PDFs?▼

Semantic chunking works with mixed content including markdown, code, and unstructured PDFs. It applies structure-aware boundary detection to maintain coherence across diverse document formats.

How do I evaluate RAG chunking quality and retrieval precision?▼

Evaluate RAG chunking quality using exposed evaluation hooks and reported quality metrics. Check retrieval precision and recall alongside coherence checks to validate production-grade pipeline performance.

Why does my document chunking lose context during retrieval?▼

Retrieval context loss often occurs when chunk boundaries ignore semantics or lack sufficient overlap. Applying 10-20% overlap with semantic boundary detection preserves coherent context across chunked documents.

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