The full answer
Be honest early: no model, and no person, is right 100% of the time. Reframe the goal from "never wrong" to "better than today, with errors caught before they cause harm". Measure how accurate the current process is, set a target per type of task, and design the system so the cases that must be right are verified by code or reviewed by a person.
1. Say it plainly, early
Anthropic's own guidance on hallucinations says the techniques reduce errors but do not eliminate them, and that critical information must be validated. Setting this expectation in the first meeting avoids a failed project later. Promising perfection is the fastest way to lose a customer's trust.
2. Measure the current baseline
Ask: how accurate is the process today? Sample past work and check it. People make mistakes too, often more than expected: copying errors, missed policy details, inconsistent decisions between staff. Measured human agreement on the task is a realistic ceiling.
3. Separate by risk
| Type of output | Approach |
|---|---|
| Low risk (drafts, summaries for internal use) | High automation; spot checks |
| Medium (customer replies on routine questions) | Automatic, with confidence-based hand-off and sampling |
| High (money, legal, health, safety) | AI prepares, a person approves; or deterministic code decides |
4. Design for catching errors
- Verify in code what can be verified: amounts against the system of record, ids that must exist, dates in order.
- Show sources with every answer so users can check quickly.
- Let the system say "I'm not sure" and hand off; measure and tune that rate.
- Make corrections easy, and feed them back into the evaluation set.
5. Agree measurable targets
"Order-status answers: 98% correct on the test set, zero wrong statuses for high-value orders (checked against the order system). Policy answers: 95% pass the rubric, all refunds over 100 euros reviewed." Targets per category, agreed with the sponsor, beat one overall number.
6. Show the trade-off in their terms
"At 95% automatic accuracy with review of the rest, your team handles 60% fewer tickets and quality goes up, because reviewers focus on hard cases." Value usually comes from speed and coverage with safeguards, not from perfection.
What interviewers want
Honesty without killing the deal, a measured baseline, and a system design that makes errors visible and cheap. "Accurate enough, with a safety net" is a plan; "100%" is a promise you cannot keep.
Last updated 16 October 2026
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