Chunking Strategies for High-Performance RAG Systems

Generative AI 14 min min read Updated: Feb 25, 2026 Advanced
Chunking Strategies for High-Performance RAG Systems
Advanced Topic 3 of 5

Chunking Strategies for High-Performance RAG Systems

Chunking determines how documents are divided before embedding. Poor chunking leads to poor retrieval.


1) Fixed-Size Chunking

Split document into equal-sized segments. Simple but may break context.


2) Semantic Chunking

Split by logical sections such as headings or paragraphs. Maintains meaning and context.


3) Overlapping Chunks

Add slight overlap between chunks to preserve continuity.


4) Ideal Chunk Size

Typically between 300-800 tokens depending on use case.


5) Summary

Chunking is not a minor step. It directly influences answer accuracy in RAG systems.

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