chunking-strategies

What problem does it solve?

Effectively splitting large documents into optimal chunks is crucial for the performance and relevance of Retrieval-Augmented Generation (RAG) systems. Suboptimal chunking can lead to poor retrieval quality and irrelevant LLM responses. This Skill provides various chunking strategies.

Core Features & Use Cases

• Chunking Methods: Implementations for Fixed-Size Chunking (with overlap), Semantic Chunking (by paragraphs), and Recursive Chunking (hierarchical splitting).
• Chunking by Document Type: Recommendations for optimal chunking strategies and sizes based on document types like technical docs, legal documents, code, and conversations.
• Chunk Enrichment: Patterns for adding metadata, LLM-generated summaries, keywords, and parent IDs to chunks for improved retrieval.
• Best Practices: Guidelines for adding overlap, preserving semantic boundaries, including metadata, and testing retrieval quality.
• Use Case: Deciding the best chunking strategy for a new document type in your RAG pipeline, implementing a recursive chunking function for long technical manuals, or enriching chunks with LLM-generated summaries for better context.

Quick Start

Use the chunking-strategies skill to generate a Python function for fixed-size document chunking with a specified overlap.