Vercel AI SDK RAG Systems

Nearly always because retrieval returned the wrong context, not because the model reasoned badly, and the two get conflated constantly. If the right passage never reaches the model, no amount of prompt tuning saves the answer. The first thing we do is measure retrieval quality on its own, and in most systems we are asked to fix, that measurement locates the problem within days.

Frequently not. Pure semantic similarity struggles with exact identifiers, product codes, names, and negation, which is why hybrid search combining keyword and vector retrieval, often with a re-ranking pass, usually outperforms it on real corpora. Which combination wins depends on your documents and your queries, so we test rather than assume.

With an explicit re-indexing strategy decided at design time: incremental updates on a change feed where your source systems can emit one, scheduled rebuilds where they cannot, and deletion handling so retired documents stop being retrieved. Stale retrieval is one of the most damaging failure modes because the system stays confident while quietly going out of date.

Yes, in almost every business context. Citations let a user verify an answer in one click, which is what makes the system trustworthy enough to actually get used, and they make debugging enormously easier because you can see exactly what the model was given. We build citation rendering in from the start rather than adding it later.