Chunking

Chunking breaks massive datasets into optimized, semantically coherent segments to maximize retrieval accuracy in RAG pipelines.

Effective chunking is the backbone of high-performance LLM applications. By partitioning documents into fixed-size segments (often 512 tokens) or recursive structures with specific overlaps (typically 10% to 20%), developers ensure that vector databases like Pinecone or Milvus return precise context. This process prevents the 'lost in the middle' phenomenon by maintaining local semantic integrity. Whether you are implementing character-based splits or advanced semantic partitioning, the goal remains the same: provide the retriever with enough signal to answer queries without exceeding the model's context window.

https://www.pinecone.io/learn/chunking-strategies/