Need to explain an AI-assisted decision later? Save the result you actually used

Our view: a decision record is more useful than a promise that rerunning the prompt will reproduce exactly the same answer.

Google’s generation-parameter guide says a fixed seed makes a best effort to repeat a response, without guaranteeing deterministic output. NIST’s AI risk framework describes documentation as supporting transparency, human review and accountability. Neither source guarantees that saving a prompt reproduces a decision.

Repetition and justification answer different questions

Repeating the wording asks whether the system returns the same text. Justifying a decision asks what information was available, which output was used, what was checked and who accepted it. My recommendation is to preserve the latter, even when a workflow usually repeats well.

A small record can be enough

For a permitted, low-stakes task, record the task, time, available model identifier, relevant settings, exact permitted input, returned output, checks and accepted revision. This is an editorial checklist, not a mandatory NIST form. If a chat service hides a setting, mark it unknown rather than inventing a value.

Compare the decision, not just the sentences

Imagine two invented drafts: “Ship after approval” and “Once approved, ship.” Both preserve the approval condition; “Ship now” changes it. A wording difference alone need not change a decision. Review the conditions and facts that matter to the task, while keeping the actual chosen text.

Keep only records you are allowed to keep

Do not turn a review trail into an uncontrolled collection of private inputs. Follow the organization’s access and retention rules. A useful record explains the accepted result to an authorized reviewer; it does not need to expose every conversation to everyone.

Published October 14, 2026. This column separates documented statements from editorial recommendations. The drafts are invented; no model rerun, seed experiment or workplace decision was tested.

Sources: Google · Generation parameters · NIST · AI RMF Core.