Originally published on AI Tech Connect.
The retrieval problem most RAG teams ignore The majority of RAG failures are not hallucination failures. They are retrieval failures. Research across enterprise document Q&A deployments consistently places the fraction of bad answers attributable to retrieval — wrong documents returned, relevant documents missed, rank order confused — at a clear majority (per production analysis, frequently cited around 70% or higher). The LLM never had a chance: it was reasoning over the wrong evidence from the start. Vector-only retrieval, which has been the de facto default since embedding models became cheap and fast, is excellent at semantic similarity. Ask a question in plain language and a well-tuned embedding model will surface conceptually related passages even when the wording is completely…
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