Originally published on AI Tech Connect.
What hybrid retrieval actually fixes The first version of almost every RAG system looks the same: embed the documents, embed the question, retrieve the nearest vectors, stuff them into a prompt. It demos beautifully and then disappoints in production, and the reason is almost always retrieval rather than the model. Dense vector search is excellent at meaning — it understands that "how do I cancel my plan" and "stopping my subscription" are the same request — but it is quietly poor at the exact tokens enterprise users actually type. Product codes, error strings, an invoice number, a clause reference, the name of a specific NHS framework or a GST circular: these are the precise terms a vector embedding tends to smooth over, because embeddings are built to generalise, not to match…
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