When this workflow helps
If you are searching for how to build a GPT for meeting decision logs, first decide who owns the result and where it will be used. A good draft saves preparation time only when the underlying facts are available and a person can inspect the output. This guide is for that specific deliverable; the Custom GPTs for Repeatable Business Tasks hub collects adjacent tasks.
Gather the source material
Approved meeting notes, attendee list, decision authority, and action register format. Use the current approved version. Remove details that the team is not permitted to place in an AI tool, or use a workspace and access controls approved for that data. Keep a link to the original material so reviewers can trace the result.
Build the first draft
Extract decisions separately from proposals and open questions. Give each confirmed action an owner, trigger, and due date only when stated. Start on one representative example, not the entire backlog. If a required fact is missing, ask for a marked gap rather than a plausible completion. Save the output as a draft with the source date and an assigned reviewer.
Starting prompt
From these notes, produce decisions, actions, and unresolved questions in separate tables. Quote the supporting line for each decision and leave unstated owners blank.
Replace the example input with your own approved documents and specify the output format your team actually uses. Ask for a short list of uncertainties alongside the draft. Keep the source and output together during review so a polished sentence does not hide a missing fact.
Review before use
The meeting chair confirms authority and circulates the log for correction before it becomes the record. Check names, dates, figures, citations, and commitments against the originals. If a consequential decision or external communication is involved, the designated human owner makes the final call and follows the normal approval path.
Measure whether it worked
Track Unassigned actions and decisions later disputed because they were only proposals. Compare a small set of completed examples with the previous process. Record corrections, not only time saved; a faster draft that creates rework is not an improvement. Revise the prompt when the same error appears twice.
Next steps
Return to Custom GPTs for Repeatable Business Tasks for related tasks, or use the AI business strategy pillar to decide where this workflow belongs in the wider business.