How do you prevent hallucinations on advice-adjacent outputs?

Retrieval grounding comes first: responses are tied to your approved knowledge base rather than to a model's recollection, and citations are required so a reviewer can check the source instead of trusting the tone. Numerical and regulatory claims are handled by deterministic guardrails rather than by the model, because arithmetic and rule lookups are not tasks that should be probabilistic.

Suitability checks and a human reviewer step sit at the end of every advice-adjacent workflow. Every system ships with a reviewer in the loop and a measurable accuracy bar agreed up front, and factuality and refusal rates are tracked as production metrics — not as launch-week diligence that quietly stops being measured.

Refusal is a designed path, not a failure. When retrieval does not support an answer, the correct behavior is to say so and route the question to a person, which is why refusal rate is measured alongside factuality. A system tuned only for factuality will learn to answer confidently on thin evidence; measuring both is what keeps that from happening.

This is also the reason the reviewer step is architectural rather than procedural. A policy that says a human should check the output is a policy someone will skip under deadline. A workflow in which nothing is sent until a reviewer acts, and in which the reviewer's action is logged, is a control.

Every AI-assisted output is logged with prompt, response, model version, and reviewer action. For regulated clients this is what makes an AI workflow defensible: FINRA and SEC 17a-4 recordkeeping, NAIC Model #275 best-interest documentation, state suitability requirements, and books-and-records retention with WORM-compliant storage all depend on being able to reconstruct exactly what was produced, from what, and who approved it.

It also matters for improvement. A logged corpus of reviewer corrections is the evaluation set for the next iteration, which is how the accuracy bar moves on evidence from your own cases rather than on a benchmark.

No control set reduces hallucination to zero. Retrieval grounding narrows what a model can draw on, citations make errors checkable, deterministic guardrails remove whole classes of failure, and a reviewer catches what gets through — but the honest statement is that the goal is a system whose failures are bounded, visible, and caught before a client sees them, not a system that cannot fail.

That is why the reviewer step and the metrics stay in place after launch. Data changes, prompts age, and model providers roll versions, and a system that met its accuracy bar in month one can slip in month six. Post-launch AI Drift Reviews exist for exactly that, billed hourly at $225 per hour with a two-hour minimum.