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chunking

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Mis à jour10 mai 2026 à 11:10

Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.

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Goodnight77

Goodnight77/rag-skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

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SKILL.md

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name	chunking
title	Chunking
description	Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
category	chunking
tags	["chunking","routing","rag","preprocessing"]
allowed-tools	["Read","Grep","Glob"]

Chunking

Overview

Use this parent skill when the main RAG problem is how to split source material into retrievable units. Route to the child skill that best matches the document shape, retrieval failure, and production constraints.

Problem Statement

Poor chunking causes retrieval misses, fragmented answers, duplicated context, and weak citations. RAG systems need chunking strategies that preserve meaning while staying efficient for indexing and retrieval.

Key Concepts

Semantic boundaries: Split where ideas naturally change.
Hierarchy: Preserve document, section, and subsection relationships.
Overlap: Add controlled neighboring context across chunk boundaries.
Context headers: Attach inherited titles and metadata to chunks.

Implementation Guide

Step 1: Identify the Corpus Shape

Use semantic chunking for prose, hierarchical chunking for structured documents, and code-specific guidance for APIs or source repositories.

Step 2: Match the Retrieval Failure

Use sliding windows when answers cross boundaries, contextual headers when chunks lack source context, and semantic chunking when fixed windows split ideas.

Step 3: Validate Chunk Quality

Measure retrieval recall, answer groundedness, duplicate context rate, and citation usefulness before changing embeddings or vector databases.

When to Use This Skill

Choosing a chunking strategy for a new RAG pipeline
Debugging poor recall caused by fragmented source content
Improving citations and source traceability

When NOT to Use This Skill

The bottleneck is ranking after retrieval
The issue is vector database operations or deployment
Source documents are already chunked and validated

Related Skills

Metrics & Success Criteria

Higher recall on chunk-boundary questions
Lower duplicate or irrelevant context in prompts
Better source citations and more coherent retrieved passages

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rag-skills

Goodnight77/rag-skills

Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.

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hierarchical-chunking

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Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.

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semantic-chunking

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Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.

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sliding-window-chunking

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Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.

2026-05-106

optimize-retrieval-latency

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Reduce retrieval latency with caching, batching, and index-level optimization.

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performance-optimization

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Route RAG performance work for latency, caching, indexing, filtering, batching, and query optimization.

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name	chunking
title	Chunking
description	Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
category	chunking
tags	["chunking","routing","rag","preprocessing"]
allowed-tools	["Read","Grep","Glob"]

Chunking

Overview

Problem Statement

Key Concepts

Semantic boundaries: Split where ideas naturally change.
Hierarchy: Preserve document, section, and subsection relationships.
Overlap: Add controlled neighboring context across chunk boundaries.
Context headers: Attach inherited titles and metadata to chunks.

Implementation Guide

Step 1: Identify the Corpus Shape

Use semantic chunking for prose, hierarchical chunking for structured documents, and code-specific guidance for APIs or source repositories.

Step 2: Match the Retrieval Failure

Use sliding windows when answers cross boundaries, contextual headers when chunks lack source context, and semantic chunking when fixed windows split ideas.

Step 3: Validate Chunk Quality

Measure retrieval recall, answer groundedness, duplicate context rate, and citation usefulness before changing embeddings or vector databases.

When to Use This Skill

Choosing a chunking strategy for a new RAG pipeline
Debugging poor recall caused by fragmented source content
Improving citations and source traceability

When NOT to Use This Skill

The bottleneck is ranking after retrieval
The issue is vector database operations or deployment
Source documents are already chunked and validated

Related Skills

Metrics & Success Criteria

Higher recall on chunk-boundary questions
Lower duplicate or irrelevant context in prompts
Better source citations and more coherent retrieved passages