Research library
Research Note #5 · Applied AI quality

How should teams test and control LLM hallucinations?

How can teams distinguish an unsupported claim from acceptable model variability and keep the risk within a business-approved limit?

Updated September 2026
A tester verification loop for detecting and controlling unsupported AI claims

Executive summary

Hallucination is not simply any unexpected wording. The critical failures are claims, citations, or conclusions that are unsupported by approved evidence or conflict with known constraints.

Method

Risk-based testing framework combining source verification, controlled datasets, adversarial prompts, abstention checks, and human escalation.

What the evidence suggests

Separate failure categories

Unsupported generation, incorrect retrieval, stale evidence, citation mismatch, policy breach, and normal wording variation need different fixes.

Ground truth has boundaries

Teams must define authoritative sources, version dates, permitted external knowledge, and cases where the system should abstain.

One percentage is insufficient

Report severity, claim type, evidence coverage, task slice, and business impact alongside an aggregate rate.

Practical test protocol

  1. 01Define authoritative evidence and acceptable uncertainty.
  2. 02Create supported, unsupported, conflicting, stale, and unanswerable cases.
  3. 03Verify every material factual claim and citation.
  4. 04Test refusal and calibrated uncertainty.
  5. 05Repeat cases and compare model or prompt versions.
  6. 06Route high-impact failures to owners and qualified human reviewers.
Control hallucination through evidence boundaries, measurable failure taxonomies, grounded workflows, and risk-based escalation—not a promise of zero errors.

Limitations

No finite test suite can prove that a generative system will never produce an unsupported answer. Controls must continue in production.

References