chunking-strategy_skill

Overview

This skill provides optimized chunking strategies for Retrieval-Augmented Generation (RAG) systems and document processing pipelines. It helps break large, multi-format documents into semantically meaningful segments that preserve context and improve retrieval quality. Use it to design chunking pipelines tailored to embedding models, vector databases, and search workloads.

How this skill works

The skill inspects document structure, content types, and information density, then recommends a chunking strategy and parameters (size, overlap, separators, similarity thresholds). It supports fixed-size, recursive character, structure-aware, semantic, and advanced methods like late chunking and contextual retrieval. It also provides validation, evaluation metrics, and iteration guidance to optimize retrieval precision, recall, and end-to-end RAG accuracy.

When to use it

• Building a RAG system that uses embeddings and vector search
• Processing large documents (PDFs, codebases, manuals) for indexing
• Optimizing low retrieval precision or high latency in existing pipelines
• Handling multi-modal content (tables, images, code) requiring special handling
• Tuning chunk size and overlap to match an embedding model’s context window

Best practices

• Start simple: fixed-size chunks (e.g., 512 tokens) with 10–20% overlap and iterate
• Preserve semantic units where possible (sections, functions, table blocks)
• Use recursive splitting for hierarchical documents (paragraph → sentence → word)
• Apply embedding-based boundary detection for thematic shifts with a clear similarity threshold
• Measure retrieval precision/recall and monitor latency and resource usage continuously

Example use cases

• Factoid QA: smaller chunks (256 tokens) with higher precision for short answer retrieval
• Analytical summarization: larger chunks (512–1024 tokens) to preserve discourse for synthesis
• Code search: structure-aware splitting that extracts functions and classes as chunks
• Domain-specific docs: semantic chunking to capture topic boundaries across long manuals
• Late chunking for long-context embedding models to reduce storage and speed up search