Most retrieval-augmented generation pipelines treat hallucination as a prompting problem. The stack gets a stricter system prompt, a lower temperature, maybe a re-ranking pass, and the team calls it mitigated. The failure mode keeps showing up anyway, because the model still answers in free-form prose no matter how firmly the prompt asks it to stick to the sources.
The alternative is to stop negotiating with the model and start constraining the output contract. A typed schema turns answer-the-question into return-these-fields, each backed by a citation id that must exist in the retrieved set. Field-level citation validation then does the quiet work: every claim lands in a declared field, every field points at a chunk that was actually retrieved, and anything the model cannot ground gets rejected at the boundary instead of shipped to the user.
Strict mode is what makes the contract real. A soft schema degrades the same way free-form answers do: the model fills the fields anyway, confidence stays high, and the garbage arrives in a clean JSON envelope. In strict mode an ungrounded field is a hard error. The request fails loudly, the retry path becomes visible in metrics, and pass rates per field turn into numbers a team can actually watch instead of vibes buried inside confident-looking paragraphs.
There is a testing dividend too. Once answers are structured, a regression suite stops being a bag of sample answers and becomes a set of comparable runs: same questions, same schema, a pass or fail per field. Schema drift shows up as a broken build rather than a slow quality leak that nobody notices until a customer forwards a wrong answer with three fabricated details in it.
We put the full engineering walkthrough on PastAGI, including how typed schema contracts handle strict-mode failures in production: Stop RAG Hallucinations With Typed Schemas.
Grounding is a data-structure problem before it is a language problem. Constrain the shape of the answer and the hallucination surface shrinks with it.
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