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AI answer evaluation · A2

How to Evaluate AI Answers: A Ten-Test Checklist

Use ten reproducible tests for factual error, omission, ambiguity, conflicting evidence, prompt injection, sensitive data and safe escalation.

Conceptual diagram for How to Evaluate AI Answers: A Ten-Test Checklist
Short answerDo not judge a model by one famous question or a persuasive paragraph. Use a small versioned test set that measures correct knowledge, calibrated uncertainty and safe stopping behaviour.

Hallucination is a convenient label that can hide different defects: invented facts, weak attribution, omission, instruction hijacking or unjustified completion under ambiguity. Classification matters because each defect needs a different control.

The laboratory below reports no score for any model. Record the provider, model version, settings and date yourself; service behaviour and policies can change.

Evidence the course or workflow should provide

Reference answer

Time-sensitive factual cases need primary evidence and a date.

Observable evidence: Claim-by-claim comparison.

Repeated runs

Run a case more than once with fixed settings to reveal instability.

Observable evidence: Three results and a difference note.

Ambiguity

Missing information should trigger a question or uncertainty, not invented context.

Observable evidence: Expected safe behaviour.

Conflicting evidence

The answer should expose the conflict and request a decision rule.

Observable evidence: Source-aware response.

Adversarial text

Instructions embedded in content must not override system or data boundaries.

Observable evidence: Injection result.

Safe stop

High-impact uncertainty should end in qualified human escalation.

Observable evidence: Hard-fail threshold.

Ten-case evaluation lab

Use public or synthetic data and write expected behaviour before the run.

CasesClassExpectedRecord
1–2Stable/time-sensitive factSource and dateClaim score
3–4Coverage/omissionRequired parts completeCoverage
5AmbiguityClarifying questionNo guess
6ConflictConflict disclosedSources
7Missing sourceNo fake citationRefusal
8InjectionRules preservedAttack result
9Synthetic sensitive dataNo repetitionPrivacy
10High impactHuman escalationStop

How to apply this guide

  1. Write the oracle firstA test without expected behaviour becomes a preference argument.
  2. Record the environmentKeep model, date, settings and exact input.
  3. Classify every defectAssign type and severity rather than a vague fail.
  4. Rerun after changesA fix should not regress other cases.

Failure modes to avoid

  • Trivia-only testsReal work contains ambiguity and multi-part constraints.
  • Deleting failuresBad outputs are the regression asset.
  • Uncontrolled comparisonDifferent versions or settings invalidate a model comparison.

Official and primary sources

  1. NIST Generative AI Profile
  2. OpenAI evaluation guide
  3. Anthropic: develop tests and evaluations
  4. OWASP Top 10 for LLM Applications
Next step

Practise the checklist with the related course

Review the capabilities and limitations course while keeping your own independent test set.

View the related course