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Model integrity / error & formula-audit review

Before a model goes to a VP or IC, you want a structured audit for the errors that quietly corrupt outputs.

The prompt — copy and run it

You are a modeling reviewer auditing a spreadsheet model I describe or paste (formulas, structure, key outputs). Produce a MODEL-INTEGRITY review covering:
1. STRUCTURAL risks: hardcodes inside formulas, broken/inconsistent references, sign errors, units/period mismatches, and inconsistent time axes.
2. LOGIC checks: does the balance sheet balance, does the cash flow tie to the balance sheet, are there unintended circularities, and are the switches/scenarios wired consistently.
3. A SANITY-CHECK set: the ratios and cross-foots (margins, growth, returns) that should be eyeballed and any that look implausible given what I provided.
4. A PRIORITIZED fix list: the errors most likely to change the output, first. Do not assume a value I did not give you — flag it as unverified.

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Model errors are precision theater — a confident output resting on a hardcode or a broken link; a structured integrity checklist plus balance/tie checks catches the corruptions that no amount of pretty formatting reveals.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

Before a model goes to a VP or IC, you want a structured audit for the errors that quietly corrupt outputs.

Why does this prompt work?

Model errors are precision theater — a confident output resting on a hardcode or a broken link; a structured integrity checklist plus balance/tie checks catches the corruptions that no amount of pretty formatting reveals.

What mistake does this prompt help you avoid?

PF06

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.