An AI assistant proposes a cause, consequence or recommendation in an engineering study. The engineer needs to judge the contribution, understand its basis and decide what belongs in the assessment.
A reviewable contribution has a clear boundary: the selected scenario, the source information and the decision expected from the engineer. The interface should make each part inspectable, alongside the draft itself.
Define exactly what the assistant can propose.
Start with a bounded contribution: a draft cause for a selected deviation, a comparison against approved reference material, or a proposed recommendation for a specific scenario. Show that scope in the interface and pass the same scope to the assistant.
Distinguish three kinds of content in the response: information drawn from a source, an inference made from that information, and a question that needs engineering input. A citation may support a source fact without supporting the inference that follows it.
NIST’s Generative AI Profile describes the risk of confidently incorrect content and recommends checking sources and citations. The practical design implication is to make the evidence inspectable at the point where the reviewer accepts or changes the draft.
Reference: NIST AI 600-1 — Generative Artificial Intelligence Profile
Keep the proposal attached to its context.
A review record should identify the selected study or asset, the relevant source revisions, the proposed text and its status. Keep the generated draft separate from the accepted engineering record. Retain the reviewer’s final wording and decision.
Context can also change while a draft is being prepared. If the selected scenario is edited or the source set is replaced, identify that change before applying the response. The reviewer should know whether the draft still refers to the work on screen.
For numerical assessments, keep the approved calculation method and its inputs explicit. AI may help a user find information or explain a recorded result; a generated number should not silently replace the calculation workflow.
- 01Context
The selected scenario and the source information available to the user.
- 02Proposal
A bounded draft with an inspectable source and inference basis.
- 03Decision
An engineer inspects, edits, accepts or rejects the contribution.
- Show which record and source set the proposal belongs to.
- Let the reviewer inspect, edit, accept or reject a draft.
- Retain accepted wording and the decision history.
- Identify changed context before applying an earlier proposal.
Test the review path with difficult cases.
Write expected behavior before testing the assistant. Include cases where the useful outcome is a question, an identified conflict or an incomplete draft. The following cases are a starting set to adapt with the engineering reviewers.
| Test case | Expected behavior |
|---|---|
| Conflicting source revisions | Identify the conflict and ask which revision is approved for the task. |
| A plausible claim with no supporting source | Expose the missing basis and keep the proposal subject to review. |
| A record outside the user’s permissions | Do not retrieve or reveal the restricted content. |
| A request for a numerical assessment | Use the defined calculation workflow and retain its inputs and method. |
| The scenario changes during generation | Identify the context change before the response is applied. |
| A failed or interrupted request | Show an incomplete attempt and preserve the current engineering record. |
Measure the effort needed to reach a decision.
Ask domain reviewers to complete representative tasks with and without assistance. Record the time spent checking evidence, correcting proposals and reaching an acceptable result. Review serious errors separately from minor wording changes.
Use feedback to improve the whole interaction. If reviewers repeatedly reopen a drawing, that context may belong beside the draft. If they cannot explain why a recommendation appeared, make the source reference and inference basis explicit in the review screen.
Maintain a set of review cases as prompts, models, reference material and integrations change. Agree which failures prevent release and who makes that decision. NIST’s AI RMF Playbook provides a broader framework for organizing that ongoing evaluation.
Reference: NIST — AI Risk Management Framework Playbook
Sources & further reading
NIST supplies the general risk and evaluation framework. The engineering review record and test cases above are Gray Foundry design recommendations for contextual assistance.
