AI for Excel
Work a full example
A worked project proves the method by showing decisions, failures, corrections, and evidence.
Before you start
Why this matters
Without opening an AI tool, write the acceptance test for this job: build and validate a June regional-revenue formula in Excel 365. Name one fact that must be exact, one judgment a person must make, and one condition that should stop the workflow. Compare your answer with the professional standard below; the gap is what you should practice.
1Learn the idea
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Project brief
See it
Role + task + context + format = clearer output
The project is to produce a tested regional monthly-summary sheet with helper checks and a short data dictionary. The user is an analyst who must fill the formula down safely. Definition of done: the intended action is clear, the candidate uses approved evidence, blocking safety checks pass, and another person can reproduce the key result.
Stage 1: prepare
Create the job card and collect column letters and headers, three sample rows, desired cell, Excel version, locale separators, date boundaries, and blank/error behavior. Remove or replace prohibited material: do not upload payroll, customer records, account numbers, hidden sheets, or an entire confidential workbook; substitute synthetic rows. Add one ordinary case, one boundary case, and one hostile or misleading case. Record unknowns instead of filling them with plausible guesses.
Stage 2: draft
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 first candidate should be 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. In this worked run, imagine it also exhibits one realistic defect from this set: wrong locale separators; text dates; shifted ranges; hidden filters; formulas filled down with relative criteria cells. Do not hide the defect. Mark the exact criterion it violates and decide whether the cause belongs to context, instruction, model capability, or the surrounding process.
Stage 3: repair narrowly
Issue a targeted revision:
Revise only the failed criterion identified below.
Preserve all verified content and the original output contract.
Do not add facts or assets.
Return the corrected artifact plus a one-line change note.
Failed criterion: [paste criterion and evidence]
A narrow repair keeps the review surface understandable. If the model cannot repair without new authoritative information, pause and obtain that information.
Stage 4: verify and release
Now 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. Record pass/fail evidence for each criterion and have the named reviewer make the release decision. 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. Save limitations in language the audience can understand.
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Retrospective
The durable deliverable is not only the final result. It is a formula test card containing schema, formula, expected rows, edge cases, and a safe-fill checklist. Write what surprised you, which check found it, what you changed, and which control should become the default. A clean retrospective distinguishes a prompt improvement from a data, tool, or policy change.
Continue learning · glossary & guides
- Can the reviewer see the failed first attempt and why the correction was justified?
- Does the release packet contain evidence, ownership, and known limitations?
- Reference · Related concept
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