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RAG Retrieval Architectures: When Better Embeddings Stop Helping

Most RAG projects start vector-first: embed the documents, store them, retrieve by similarity. It works in the demo. Then a user searches for an exact thing, a product code, an error number, a…

  • Vector-first RAG systems fail to retrieve exact information like product codes or error numbers.
  • Hybrid search combines lexical query (BM25) and vector query to recover missed results.

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