Chunking Strategies for RAG

LangChain's hierarchical text chunking strategy achieving 85-90% accuracy by recursively splitting using progressively finer separators to preserve semantic boundaries.

The process of determining optimal text segment sizes for embedding and retrieval in vector databases. Chunk size significantly impacts RAG quality, balancing between capturing complete context (larger chunks) and retrieval precision (smaller chunks), typically ranging from 256 to 1024 tokens.

An advanced RAG chunking strategy that uses LLMs to dynamically determine optimal document splitting based on semantic meaning and content structure. Agentic chunking analyzes document characteristics and adapts the chunking approach per document for superior retrieval accuracy.

A RAG enhancement technique from Anthropic that adds chunk-specific explanatory context to each document chunk before embedding. Contextual Retrieval reduces retrieval failure rates by 49% and improves accuracy by 67% compared to traditional RAG methods.

Advanced document chunking approaches that combine multiple chunking methods (fixed-size, semantic, structural) to optimize retrieval in RAG systems. Hybrid strategies adapt to document characteristics for superior performance.

Critical preprocessing step for RAG systems involving extraction of text, tables, and images from various document formats (PDF, DOCX, HTML) using tools like Unstructured, LlamaParse, and PyPDF.