Chunking Strategies for High-Performance RAG Systems in Generative AI
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.
