Graph-Enhanced RAG for Multi-Hop Web Knowledge Retrieval
Graph-enhanced RAG chains evidence across documents where flat vector search falls short.
Graph-enhanced RAG chains evidence across documents where flat vector search falls short.
RAGAS excels at static corpora but misses four critical dimensions that web retrieval demands.
Benchmark scores mislead; test embedding models on your actual messy web corpus.
Stale embeddings deliver confident wrong answers without triggering alerts.
Treating retrieval as one stage instead of five is where production RAG systems fail.
Reranking alone won't save your RAG system without strategic document placement.
Combining keyword and semantic search beats either approach alone on diverse queries.
Multiple chunking strategies work best depending on document structure, not one method for all.
Stopping hallucinations by grounding each agent step in retrieved facts.