AI answers can be useful, incomplete, or wrong in different ways. Review effort should match what could happen if an error is missed. This framework helps a team organize evaluation; it does not supply a universal score, benchmark, or certification.
Tier 1: Low-impact drafts
Examples include brainstorming titles or reorganizing non-sensitive notes. Review for relevance, tone, and unsupported factual additions. Keep a person responsible for the final output.
Tier 2: Work that affects a decision or external communication
Check factual claims against original sources, inspect citations, confirm required context, and review for privacy or bias concerns. Document who approves the final action.
Tier 3: High-impact or irreversible uses
Do not rely on a general-purpose answer as the decision-maker. Require qualified human review, representative evaluation data, documented access controls, an escalation route, and an incident response plan. Follow applicable laws and organizational policy.
Build a task-specific rubric
- Define the user, intended purpose, and prohibited use.
- List normal, ambiguous, and adversarial examples.
- Set criteria for correctness, completeness, source support, refusal, privacy, and escalation.
- Assign reviewers and describe what evidence they should inspect.
- Reassess when a model, prompt, data source, or connected tool changes.
Separate dimensions instead of hiding them in one score
Accuracy, usefulness, fairness, privacy, latency, and cost are different questions. A high average on an easy set can conceal a dangerous failure on a rare case. Report individual failure modes and the limits of the evaluation set.
Turn the risk tier into a review decision
A useful evaluation record connects a task to an action someone can take. Record the intended user, allowed input, expected output, consequence of an error, reviewer, evidence to inspect, and what happens when the answer is uncertain. Keep these as separate fields; a single average score can hide a failure that matters.
- Define the decision. State whether the model is drafting, retrieving, recommending, or changing something. Name the human who remains accountable.
- Write representative cases. Include an ordinary request, missing context, conflicting evidence, an out-of-scope request, and a privacy-sensitive input that should be refused or redirected.
- Set a pass condition per case. For example, a source-finding task must link to the relevant record and distinguish a quotation from an inference. Do not count fluent wording as evidence.
- Route failures. Decide whether the system may retry, must ask for context, or must stop for human review. An unsafe answer should not become acceptable because the other examples passed.
- Re-evaluate changes. Repeat the same cases when a model, prompt, connected tool, or source collection changes, and note what changed.
Illustrative review record
Suppose a fictional assistant summarizes a sample policy and states that “requests must be filed by 12 May.” The fictional source says “13 May.” Mark the claim contradicted, retain the source passage, correct the draft, and investigate whether the error affects other dates. This is a made-up teaching example, not a model test or a reported result.
For organizational risk framing, consult the NIST AI Risk Management Framework and its Generative AI Profile. They are voluntary guidance, not a certification or a substitute for domain-specific review.
Related reading
To verify claims in a single response, use the claim-by-claim fact-check method. For research from question through source ledger, see the research workflow guide.
About the author
Femica Maydinda Harend writes practical technology guides for AILooma with emphasis on evidence quality and clear accountability.
Editorial note
This is a general evaluation structure, not a substitute for domain expertise, a formal risk assessment, or required compliance review.