Chunking Strategies for Long-Form Web Documents
Chunking strategy matters more than embedding model choice for retrieval quality.
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10 stories in RAG Architecture Patterns.
Chunking strategy matters more than embedding model choice for retrieval quality.
Graph-based retrieval handles multi-hop questions vector search alone cannot answer correctly.
Four variables determine whether RAG or long-context windows will cost less and work better.
RAGAS separates retrieval failures from generation failures without labeled answer keys.
Separate indexes enforce isolation at the storage layer, not the prompt.
Cross-encoders beat single-vector retrieval by rescoring candidates jointly instead of in isolation.
Combining dense vectors with BM25 retrieval fixes the two ways RAG systems fail most often.
Filtering retrieved documents by metadata slashes hallucination rates by half or more.
Decide staleness tolerance per topic, not per pipeline.
Smaller chunks excel at factoid queries while larger ones catch thematic relationships.