Pilot of an LLM for Regulatory Acceptance Test Drafts
ClaimsCo, a health insurer, is piloting a vendor-hosted LLM to draft acceptance tests for a claims portal with 1,200 regulatory requirements. The release is in three weeks. The vendor claims 95% requirement coverage and says no customer data is retained, but the contract is still under legal review. Testers paste requirement paragraphs and occasional screen captures into the chat interface because the tool has no repository integration. The drafts look polished and include clause references, expected benefit amounts, and step-by-step workflows. A spot check finds 12 invented clause numbers, 4 expected amounts that contradict the payment rules, and one draft containing a member name visible in a screenshot. The test manager must decide whether these drafts can be used as audit evidence and whether to expand the pilot to 200 requirements. The team has limited AI evaluation experience and no golden dataset.
1. Assess the risks of using these LLM drafts as audit evidence and decide whether the pilot should be expanded to 200 requirements. Justify the decision.
Read the worked answer
Reasoning. Polished prose is not evidence. The invented clause numbers are hallucinations; the contradictory benefit amounts are factual errors; the member name is a data leak. Audit evidence needs source traceability, reproducibility, reviewer accountability, and a defensible link from requirement to test. The current workflow has none of these because the tool is not integrated with the requirement repository and the contract is unresolved.
Decision. Do not treat the drafts as sole audit evidence, and do not expand to 200 requirements yet. Use them only as drafts with a named human reviewer, requirement IDs, and source citations that a reviewer verifies. Expanding now would multiply rework and privacy exposure before the team can measure error rates. A controlled pilot is justified only after the evaluation and data-handling controls in the next tasks are in place.
2. Define a pre-expansion evaluation with concrete metrics, thresholds, and human review steps.
Read the worked answer
Build a golden set of 40–50 representative requirements, including 10 seeded traps: non-existent clauses, conflicting rules, missing dates, and a screenshot containing fake PII. Have two experienced reviewers independently score raw model output and post-review output.
Metrics should include: citation validity (percentage of cited clauses that exist and support the assertion), factual consistency against the payment rules, requirement coverage, false-pass rate on seeded traps, PII leakage incidents, reviewer minutes per accepted test, and inter-reviewer agreement. Set zero tolerance for PII in output. Require 100% of accepted tests to have a resolvable source requirement and reviewer sign-off; raw citation validity should be high enough that review is practical, not merely cosmetic.
Human review must check expected results against authoritative rules, not just readability. Record rejected drafts and reasons so the team can improve prompts and measure whether the model is safe for a larger pilot.
3. Recommend prompt and data-handling controls before any further use.
Read the worked answer
Data handling: stop pasting screen captures and real member data. Redact or replace identifiers with synthetic values. Require a data protection agreement, no-training-on-inputs terms, retention limits, and a private or enterprise endpoint. Add a DLP check before prompts leave the team.
Prompting: retrieve only approved requirement text and pass it as context. Instruct the model to cite source IDs only, never invent clause numbers, and output “insufficient source” when a rule is absent. Use a structured output schema with fields for requirement ID, preconditions, steps, expected result, and source citation. Lower randomness and log prompts and outputs for audit.
Process control: a human reviewer must verify every citation and expected amount against the source. The model is an idea generator, not an oracle. These controls reduce hallucination and leakage risk, but they do not make the output audit evidence by themselves.