Originally published on AI Tech Connect.
What this recipe gives you Most RAG systems that "just do not retrieve well" have a fixable root cause: the embedding model was trained on the open web and has never seen your domain. It does not know that in your world NAV means net asset value and not navigation, that PDI is a pre-delivery inspection and not a statistical divergence, or that a query about "the section 80C limit" should pull an Indian tax document rather than an American 401(k) explainer. General-purpose embeddings map your jargon to roughly the wrong region of vector space, and no amount of clever chunking rescues a retriever that cannot tell your relevant passages apart from your near-misses. Fine-tuning the retrieval models fixes this at the source. It is cheaper than most people assume — a small embedding model…
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