What is the benefit of semantic chunking over fixed-size chunking?

Semantic chunking uses embedding-based boundary detection to ensure segments represent complete thoughts, preventing the context loss that occurs when a fixed-size split cuts through the middle of a sentence or logical block.

Does this skill support Late Chunking?

Yes, it includes Level 5 advanced methods such as Late Chunking for use with long-context embedding models to preserve global document context.

Can this skill handle source code specifically?

Yes, it includes Level 3 Structure-Aware strategies designed to identify and preserve semantic units like functions, classes, and logic blocks in various programming languages.

How do I determine the best chunk size for my project?

The skill provides a rubric based on query type: typically 256 tokens for factoid-based queries and 1024 tokens for analytical queries requiring more context.

RAG Chunking Strategy

Name: RAG Chunking Strategy
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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データサイエンスとML

Implements optimal document chunking strategies to enhance RAG system retrieval accuracy and maintain semantic context.

概要

This skill provides a comprehensive framework for breaking large documents into semantically meaningful segments for vector databases and RAG pipelines. It guides developers through five levels of strategy complexity—ranging from basic fixed-size splitting to advanced embedding-based semantic boundary detection. By optimizing how data is ingested into vector stores, this skill ensures that Claude and other LLMs receive the most relevant, coherent context, directly improving the precision of AI-generated responses and reducing hallucination in document-heavy applications.

主な機能

44 GitHub stars
Structure-aware splitting for code, Markdown, tables, and multi-modal PDFs
Performance evaluation framework using retrieval precision and recall metrics
Five-tier strategy implementation from simple fixed-size to advanced semantic chunking
Recursive character splitting with hierarchical separators for structural preservation
Support for advanced methods like Late Chunking and Contextual Retrieval

ユースケース

Implementing structure-aware code splitting for AI-driven software maintenance tools
Building high-precision RAG systems that require thematic consistency in retrieved segments
Optimizing vector search performance in high-density technical documentation repositories

概要

主な機能

44 GitHub stars
Structure-aware splitting for code, Markdown, tables, and multi-modal PDFs
Performance evaluation framework using retrieval precision and recall metrics
Five-tier strategy implementation from simple fixed-size to advanced semantic chunking
Recursive character splitting with hierarchical separators for structural preservation
Support for advanced methods like Late Chunking and Contextual Retrieval

ユースケース

Implementing structure-aware code splitting for AI-driven software maintenance tools
Building high-precision RAG systems that require thematic consistency in retrieved segments
Optimizing vector search performance in high-density technical documentation repositories