Use prompt moves that transfer

First, orient the model with the real audience and decision. Second, ground it in supplied sources. Third, constrain scope, format, and forbidden actions. Fourth, inspect by asking for assumptions, unsupported claims, or tests. Applied to this topic, those moves support run a thirty-minute weekly practice loop that improves one real workflow, not vague content generation.

This week I want to improve meeting-note follow-up. Baseline: 25 minutes and occasional missed owners. Design one 30-minute practice: a sanitized sample, one constrained prompt, a checklist for owner/date/source accuracy, and a five-minute retrospective. Keep the tool fixed and change only one prompting variable.

The likely useful output is: A bounded weekly experiment with a baseline, one controlled change, an output check, and a decision to keep, revise, or discard the technique. Follow with a critic pass, not a request to “improve it”:

Audit the draft against the original contract. Return a table:
criterion | pass/fail | exact evidence | smallest correction.
Do not introduce new facts. List unresolved questions separately.

This second prompt changes the mode from creation to inspection. For alternatives, request deliberately different options and specify the axis of difference. For revision, name one defect and freeze everything else. For extraction, require a schema and define unknown/null behavior. For decisions, ask for criteria, evidence, assumptions, and sensitivity—not hidden private reasoning.

A useful response would look like this: A bounded weekly experiment with a baseline, one controlled change, an output check, and a decision to keep, revise, or discard the technique. That description is intentionally observable. “Looks good” is not acceptance. The operator must compare against the baseline, verify every owner and due date against source notes, record failure cases, and repeat on a second sanitized example before adopting. Keep the source material beside the draft so review means comparison, not memory.

Do not confuse fluent explanations with evidence. Consistency beats novelty. One checked experiment each week produces transferable judgment; browsing new tools without measuring work does not. The prompt is successful only when the resulting artifact survives an external check.

Watch for collecting prompts without testing; changing tool and task simultaneously; counting speed while quality falls; skipping reflection; automating before understanding. If the answer is too broad, shrink the deliverable. If it invents, tighten “use only” boundaries and require source labels. If formatting drifts, provide a short valid example and validate mechanically. If every option sounds alike, define meaningful axes. If revision damages good sections, quote the exact passage to preserve.

Keep prompt versions with short notes: what changed, why, and what happened. That creates transferable knowledge. Copying a “perfect prompt” without its data, risk level, and reviewer rarely does.