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 build and validate a June regional-revenue formula in Excel 365, not vague content generation.

Excel 365, comma separators. A=Date, B=Customer, C=Region, D=Revenue; F2 contains a region. In G2, total D where C=F2 and A is in June 2026. Give one formula, explain every condition, and provide normal, boundary, and blank/error tests. Do not invent columns.

The likely useful output is: A SUMIFS formula using >=DATE(2026,6,1) and <DATE(2026,7,1), with an explanation that the exclusive upper bound includes every June timestamp. 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 SUMIFS formula using >=DATE(2026,6,1) and <DATE(2026,7,1), with an explanation that the exclusive upper bound includes every June timestamp. That description is intentionally observable. “Looks good” is not acceptance. The operator must calculate a tiny hand-checked table, test June 1 and June 30, inspect dates stored as text, confirm absolute references, and compare the total with a filtered sum. Keep the source material beside the draft so review means comparison, not memory.

Do not confuse fluent explanations with evidence. Keep raw columns untouched. Use helper columns for cleanup so every transformation is reversible, and never accept a formula whose result you cannot reproduce on five known rows. The prompt is successful only when the resulting artifact survives an external check.

Watch for wrong locale separators; text dates; shifted ranges; hidden filters; formulas filled down with relative criteria cells. 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.