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ai guide

LongRAG: Rethinking RAG Chunking Strategy with Long-Context Models

Traditional RAG splits documents into small chunks for retrieval, but this causes information fragmentation. LongRAG leverages 100K+ token long-context models to retrieve larger document segments (entire sections or even whole documents), reducing fragmentation while maintaining retrieval efficiency.

#rag#longrag#long-context#chunking#retrieval
ai guide

Chunking Strategies: How You Split Text Determines Whether RAG Can Find the Answer

Chunks too large and retrieval loses precision; too small and you lose context. Chunking is the most underrated part of RAG — pick the wrong strategy and no amount of downstream optimization will save you.

#rag#chunking#indexing#text-splitting#retrieval
ai guide

Contextual Retrieval: Giving Every Chunk Its "What This Is About" Context

When you split a document into chunks, each chunk loses its place in the original document. Contextual Retrieval solves the isolated-chunk problem by injecting a document-level summary into every chunk at index time.

#rag#contextual-retrieval#chunking#indexing#embedding