AI costing prompts · Read this first
How to stop AI inventing your numbers
A large language model does not look anything up. It predicts the most plausible next words, and a plausible-looking number is exactly the kind of text it is good at producing. In most writing that is harmless. In finance it is dangerous, because a fabricated figure that reads like a real one will pass straight into a model, a forecast or a board pack before anyone questions it. This page is the discipline that stops that happening.
In short
The fix is not a better model or a cleverer trick; it is a short set of guardrail instructions you add to every costing prompt. They force the AI to work only from the data you give it, to show its formulas, to flag what is missing, and to admit when it does not know. Use the seven below individually, or paste the combined block at the foot of this page in front of any prompt. They turn a confident guesser into a careful assistant.
Why it happens
This is not a bug that a future version will quietly remove. It is inherent to how language models work. They are trained to generate text that is statistically likely given everything before it, with no built-in concept of whether a statement is true or whether a number was actually computed. When your prompt leaves a gap, the model fills it with whatever is most plausible, and "plausible" and "correct" are not the same thing. Independent research has reported hallucination rates roughly in the range of 15 to 25 percent on financial and numerical tasks when no safeguards are in place; that figure is approximate, varies widely by model and task, and should be verified rather than quoted as precise. The direction of the finding is the point: unguarded, these tools invent at a rate that matters in finance.
We tested it ourselves before writing this. Given a vague instruction, "build me a TDABC model," with no data attached, the model produced a complete, confident model: it invented a department cost, a headcount, and a per-minute capacity rate, none of which existed, and presented them with the same calm authority it would use for a real result. Nothing in the output signalled that the foundation was fiction. That is precisely the failure mode to guard against, because the more polished the answer looks, the less likely anyone is to check it.
The seven guardrails
1. Restrict it to the data you provide
The single most important instruction. It removes the gaps the model would otherwise fill with invention.
Work only from the data I give you. Do not invent any numbers, rates or volumes.
2. Make it flag every assumption
When a model must assume something, you want it said out loud, not buried in the arithmetic.
List every assumption you make. If a figure is missing, label it DATA MISSING and tell me what you need.
3. Make it show the formula before the number
A visible formula is checkable; a bare number is not. This also catches arithmetic slips.
Show the formula at each step before computing any value.
4. Give it permission to say "I do not know"
Models invent partly because they are nudged to be helpful. Explicitly allow a non-answer.
If you cannot derive a figure from my data, say so. Do not estimate.
5. Make it cite the source line
Tying every number back to a row in your data makes fabrication obvious, because invented numbers have no source.
For each number, cite the exact source row in my data.
6. Make it separate fact from inference
You need to know which parts are your data, which are assumptions, and which are the model's opinion.
Clearly separate what you calculated from my data, what you assumed, and what is your suggestion.
7. Make it verify the totals
A reconciliation check at the end catches both invention and arithmetic error in one pass.
After the calculation, check that the parts sum to the total and flag any discrepancy.
The full guardrail block
Paste this once at the top of any costing prompt. It combines all seven into a single instruction the model must follow before it touches your data.
Before answering, follow these rules for the entire response: 1. Work only from the data I give you. Do not invent any numbers, rates or volumes. 2. List every assumption you make. If a figure is missing, label it DATA MISSING and tell me what you need. 3. Show the formula at each step before computing any value. 4. If you cannot derive a figure from my data, say so. Do not estimate. 5. For each number, cite the exact source row in my data. 6. Clearly separate what you calculated from my data, what you assumed, and what is your suggestion. 7. After the calculation, check that the parts sum to the total and flag any discrepancy.
A guardrail reduces risk; a real model removes it
This is exactly the discipline we apply when we build a cost model: work only from reconciled data, show every formula, flag every assumption, tie every number to a source. A prompt guardrail lowers the chance of a fabricated figure, but it cannot reconcile your ledger, validate your drivers or own the result. Only a model built on your real, reconciled data does that. If the numbers matter enough to protect, build them on something solid.