Audit Excel headers, field definitions, and data types

Author: AILesson7 min setupTested with:ChatGPTReviewed: 2026-08-28

Quick answer

Compare a workbook with its intended schema before import, analysis, or automation. Provide: Workbook purpose and consumer, Expected schema, Observed headers and sample values. Expected result: A schema dictionary, conformance report, and row-level validation plan.

Add your context

Your text stays in this browser. AILesson Prompts does not send it to a model or server.

Workbook purpose and consumerExpected schemaObserved headers and sample values

Your prompt

Unfilled fields remain visible as placeholders, so you can still copy and edit the prompt

Audit an Excel workbook against its intended schema.

Purpose and downstream consumer:
[purpose]

Expected schema:
[expected]

Observed headers and representative values:
[observed]

Check header names, order only when order matters, missing and unexpected fields, data types, date and number storage, requiredness, uniqueness, allowed values, and cross-field rules. Distinguish cell formatting from underlying value type. Return a data dictionary, schema-difference table with severity, row-level validation formulas, blocking issues, and an acceptance checklist. Do not declare conformance from display appearance alone.

Try in Playground

Private by defaultPrompt assembly happens locally in your browser. Avoid placing confidential information into any AI service unless your organization allows it.

From input to outcome

A worked example

See how concrete context turns this recipe into a usable result

Actual input

Workbook purpose and consumer: Upload weekly orders to an operations system. Column order is irrelevant, but exact header names and field types are required.
Expected schema: OrderID required unique text; OrderDate required Excel date; CustomerID required text; Amount required number >=0; Currency required one of USD, EUR, GBP; Cancelled optional TRUE/FALSE. No extra fields containing notes may be uploaded.
Observed headers and sample values: Headers: Order ID, OrderDate, CustomerID, Amount, Currency, Cancelled, InternalNotes. Samples include Order ID 00125; OrderDate displayed 08/27/2026 but imported as text in some rows; Amount '$1,240.00' stored as text; Currency usd; Cancelled Yes.

Example output

Schema differences

Issue	Severity	Evidence	Required action
Order ID header differs from OrderID	Blocking	observed space	rename exactly
InternalNotes is unexpected	Blocking/privacy	extra sensitive-capable field	exclude from upload
OrderDate mixed type	Blocking	some values are text	convert and validate, do not only reformat
Amount stored as currency text	Blocking	$ and comma in text	parse under confirmed locale, retain raw
Currency uses lowercase	Fixable	usd	map approved case to USD
Cancelled uses Yes	Blocking until mapped	contract requires Boolean	approve Yes/No mapping or reject

Create a data dictionary with field, definition, stored type, required, allowed values, example, and validation. Row checks can include =AND(ISTEXT([@OrderID]),[@OrderID]<>""), =ISNUMBER([@OrderDate]), =AND(ISNUMBER([@Amount]),[@Amount]>=0), =ISNUMBER(XMATCH([@Currency],{"USD","EUR","GBP"})), and =OR([@Cancelled]=TRUE,[@Cancelled]=FALSE,[@Cancelled]=""). Check uniqueness with COUNTIF. Accept only when exact required headers exist, InternalNotes is excluded, every row passes type and domain checks, OrderID count equals unique OrderID count, and rejected rows are listed separately. Preserve 00125 as text.

Why this works

1
A schema contract catches structural failures before they become misleading analysis results.
2
Testing underlying value types reveals dates and numbers that only look correctly formatted.

Check the result

Are required fields, keys, and allowed values explicitly documented?
Do validation checks inspect stored types rather than display formats?
Are blocking import failures separated from nonblocking presentation differences?

Use it with confidence

Frequently asked questions

Practical answers about when to use this recipe, what to provide, and where human review still matters

What should I prepare before using “Audit Excel headers, field definitions, and data types”?

For “Audit Excel headers, field definitions, and data types,” prepare Workbook purpose and consumer, Expected schema, and Observed headers and sample values. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Audit Excel headers, field definitions, and data types” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A schema dictionary, conformance report, and row-level validation plan—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Audit Excel headers, field definitions, and data types”?

The published test record for “Audit Excel headers, field definitions, and data types” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

More ways to explore

Where this recipe fits

Text prompts Feedback & review Excel & spreadsheets

Keep the work moving

Text prompt

Spreadsheet insights

Excel & spreadsheets 7 min setup

Audit a chart for misleading design

Review scale, encoding, labels, missing context, and the claim against source values

Open recipe

Text prompt

Spreadsheet insights

Excel & spreadsheets 8 min setup

Analyze survey ratings and comments in Excel

Combine rating distributions with traceable comment themes and response-bias limits

Open recipe

Text prompt

Spreadsheet insights

Excel & spreadsheets 6 min setup

Turn spreadsheet analysis into an executive summary

Convert verified metrics into decisions, changes, risks, and requests without overstating evidence

Open recipe

AILesson · Recommended courses

Your next step: put AI to work

Go from understanding AI to getting things done. Practice prompting, reviewing, and improving results through interactive lessons for work and everyday life.

Explore all courses