7 Chunking Strategies That Decide Whether Your RAG Works
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Naive fixed-size token chunking is one of the most common causes of poor RAG performance. Seven chunking strategies are compared: fixed-size token chunking with overlap, sentence-window retrieval, document-aware structural chunking, semantic embedding-based chunking, hierarchical parent-child chunking, agentic LLM-driven propositional chunking, and multi-modal table-preserving chunking. Each strategy is explained with how it works, its tradeoffs, and when to use it. Beyond chunking, production RAG systems also require index lifecycle management, deterministic UUIDs based on content hashes, TTL policies, and chunk deduplication to prevent vector database bloat and degraded LLM reasoning.
