Chunking Methods

Chunking Methods segment large documents into optimized, context-rich data blocks for efficient vector database indexing and precise Retrieval-Augmented Generation (RAG).

This is a critical preprocessing step for LLM applications: you break massive text corpora into smaller, coherent chunks. The goal is simple: maximize retrieval relevance while respecting the model's token limit (e.g., 8196 tokens). Key strategies include **Fixed-Size Chunking** (using a set character or token count, like 500 tokens with overlap), **Recursive Chunking** (splitting hierarchically by separators), and **Semantic Chunking** (using embedding models to identify natural topic breaks). Proper chunking directly dictates the accuracy and efficiency of your RAG system's output.

https://www.pinecone.io/learn/chunking-strategies-for-llm-applications/