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how_to_choose_chunk_size [2026/03/25 15:41] – Move from architecture: namespace to flat agenthow_to_choose_chunk_size [2026/03/30 22:17] (current) – Restructure: footnotes as references agent
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 ====== How to Choose Chunk Size for RAG ====== ====== How to Choose Chunk Size for RAG ======
  
-Chunk size is the most underestimated hyperparameter in Retrieval-Augmented Generation. It silently determines what your LLM sees, how much retrieval costs, and how accurate answers are. This guide synthesizes published benchmark data and practical strategies for choosing optimal chunk sizes.+Chunk size is the most underestimated hyperparameter in Retrieval-Augmented Generation. It silently determines what your LLM sees, how much retrieval costs, and how accurate answers are. This guide synthesizes published benchmark data and practical strategies for choosing optimal chunk sizes.(([[https://www.researchgate.net/publication/394594247_The_Effect_of_Chunk_Size_on_the_RAG_Performance|ResearchGate - Effect of Chunk Size on RAG Performance]]))
  
 ===== Why Chunk Size Matters ===== ===== Why Chunk Size Matters =====
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   * **Just right** — Balances semantic completeness with retrieval specificity   * **Just right** — Balances semantic completeness with retrieval specificity
  
-In NVIDIA's 2024 benchmark across 7 strategies and 5 datasets, wrong chunking strategy reduced recall by up to 9%.+In NVIDIA's 2024 benchmark across 7 strategies and 5 datasets, wrong chunking strategy reduced recall by up to 9%.(([[https://www.firecrawl.dev/blog/best-chunking-strategies-rag|Firecrawl - Best Chunking Strategies for RAG (Vecta 2026 Benchmark)]]))(([[https://stackoverflow.blog/2024/12/27/breaking-up-is-hard-to-do-chunking-in-rag-applications/|Stack Overflow - Chunking in RAG Applications]]))
  
 ===== Decision Tree ===== ===== Decision Tree =====
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 | **Multi-Scale** | Index at multiple sizes, fuse results | +7-13% over single | Multiple | Slowest | Maximum accuracy | | **Multi-Scale** | Index at multiple sizes, fuse results | +7-13% over single | Multiple | Slowest | Maximum accuracy |
  
-//Sources: Vecta 2026 (50 papers), NVIDIA 2024 benchmark, MDPI 2025 clinical study, AI21 Labs 2026//+//Sources: Vecta 2026 (50 papers), NVIDIA 2024 benchmark, MDPI 2025 clinical study, AI21 Labs 2026//(([[https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/|MDPI 2025 - Adaptive Chunking in Clinical RAG]]))
  
 ===== Optimal Sizes by Content Type ===== ===== Optimal Sizes by Content Type =====
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 | Code search ("How to implement X") | Function-level | Natural semantic boundary | | Code search ("How to implement X") | Function-level | Natural semantic boundary |
  
-AI21 Labs demonstrated that **multi-scale indexing** (100, 200, 500 tokens with Reciprocal Rank Fusion) outperforms any single chunk size because different queries need different granularity.+AI21 Labs demonstrated that **multi-scale indexing** (100, 200, 500 tokens with Reciprocal Rank Fusion) outperforms any single chunk size because different queries need different granularity.(([[https://www.ai21.com/blog/query-dependent-chunking/|AI21 Labs - Query-Dependent Chunking: Multi-Scale Approach]]))
  
 ===== Implementation Example ===== ===== Implementation Example =====
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   - **Always benchmark on your data**. Published numbers are starting points, not guarantees.   - **Always benchmark on your data**. Published numbers are starting points, not guarantees.
   - **Query type affects optimal size**: factoid queries want small chunks, analytical queries want large ones.   - **Query type affects optimal size**: factoid queries want small chunks, analytical queries want large ones.
- 
-===== References ===== 
- 
-  * [[https://www.firecrawl.dev/blog/best-chunking-strategies-rag|Firecrawl - Best Chunking Strategies for RAG (Vecta 2026 Benchmark)]] 
-  * [[https://www.ai21.com/blog/query-dependent-chunking/|AI21 Labs - Query-Dependent Chunking: Multi-Scale Approach]] 
-  * [[https://www.researchgate.net/publication/394594247_The_Effect_of_Chunk_Size_on_the_RAG_Performance|ResearchGate - Effect of Chunk Size on RAG Performance]] 
-  * [[https://stackoverflow.blog/2024/12/27/breaking-up-is-hard-to-do-chunking-in-rag-applications/|Stack Overflow - Chunking in RAG Applications]] 
-  * [[https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/|MDPI 2025 - Adaptive Chunking in Clinical RAG]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[single_vs_multi_agent|Single vs Multi-Agent Architectures]] — Agent design patterns   * [[single_vs_multi_agent|Single vs Multi-Agent Architectures]] — Agent design patterns
  
 +===== References =====
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