RAG Chunking Strategist

Designs optimal text chunking strategies for Retrieval-Augmented Generation (RAG) systems, balancing chunk size, semantic coherence, retrieval precision, and generation quality across diverse document types.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.3

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.4

Completeness (10%)

8.3

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Optimizing chunk sizes for technical documentation RAG systems with complex cross-references
▸Designing semantic chunking strategies for legal document retrieval with clause-level precision
▸Implementing variable-size chunking for mixed-format corpora (PDFs, HTML, code, markdown)
▸Troubleshooting poor retrieval quality in production RAG systems through chunking analysis
▸Building document preprocessing pipelines that preserve context while maximizing retrieval accuracy

What you'll get

●Detailed chunking configuration with specific token ranges, overlap percentages, and splitting hierarchy for document type
●Comparative analysis of chunking approaches with expected precision/recall trade-offs and implementation complexity
●Step-by-step chunking pipeline design with preprocessing steps, validation checks, and quality metrics

Not designed for ↓

×General text preprocessing or NLP tokenization tasks unrelated to RAG retrieval
×Vector database selection or embedding model fine-tuning decisions
×LLM prompt engineering or generation quality optimization beyond chunking impact
×Basic document parsing or OCR text extraction workflows

Expects

Document corpus characteristics (types, structure, length, density) and retrieval quality requirements with specific use case context.

Returns

Detailed chunking strategy with specific parameters, implementation approach, and expected performance trade-offs for the given corpus.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

ragchunkingtext-splittingembeddingsretrievalvector-searchlangchainllamaindexdocument-processingsemantic-searchnlpllm

Research Foundation: 8 sources (3 official docs, 1 industry frameworks, 2 web, 2 academic)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

Embedding Space ArchitectPlatinum NLP Pipeline ArchitectGold RAG Architecture DesignerGold Search Relevance EngineerPlatinum Vector Database OptimizationGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Hybrid Search ArchitectGold LLM Context Window OptimizerGold

Common Workflows

RAG System Optimization

Complete RAG system design from document preprocessing through retrieval optimization

rag-chunking-strategist→Embedding Space Architect→Vector Database Optimization→RAG Architecture Designer

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