A wrong answer from the model would cost real money. How do you put it in the product anyway?
Price the error first, then shrink what the model decides: code applies policy and computes amounts, answers cite sources, and checks run against systems of record. Route a case to human review when its measured error rate makes review cheaper than the expected loss, and launch on an eval sized for that claim.
Answer
Short answer
Price the error before you write a prompt. Then narrow what the model decides: it extracts and classifies, while code applies policy and computes any amount. Guardrails sit around it as grounding in cited sources and mechanical checks against your systems of record. Send a case to human review when its measured error rate makes review cheaper than the expected loss, and launch only on an eval large enough to support the error rate you claim.
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Likely follow-ups
- Reviewers overturn almost nothing for three months. Is the model that good, or have the reviewers stopped looking?
- How do you set the threshold for a segment that has too few labelled cases to measure?
- Which actions would you never automate however good the eval looks, and why?
- After launch, how do you learn the error rate of cases nobody reviewed?
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