The Best AI Products Know When To Call A Calculator

Mateusz Mucha is CEO of Omni Calculator, a platform helping 15M+ monthly users make better decisions through expert-reviewed calculators.

​A large language model (LLM) doesn’t calculate. It predicts text one token at a time, so any number it returns is a guess. And good products know when to hand the math to a tool that computes it instead.

I ask one question before building with an LLM.

At my company, before we build anything where the model produces a value the user acts on, I ask one question: Does the output have to be reproducible? If running the same request 100 times has to return the same answer every time, that answer must come from a deterministic tool the model calls rather than from the model itself.​

As an example, for our Omni Calculator Builder, users describe the calculator they want in plain language, an LLM turns that description into calculator logic and that logic runs on our deterministic math engine. The LLM designs the tool, but it never produces the final number.

Similar examples also handle the phrasing while ​a separate engine produces what has to be exact. If you install the Wolfram connector in Claude, the model will route math and unit-conversion questions to Wolfram’s computation engine. Claude rewrites the relevant part of the query in Wolfram Language, Wolfram computes the result and Claude builds its response around that number instead of predicting it.

Of course, not every use of an LLM has to clear that bar. Tasks such as drafting ad copy or writing an email are open-ended and don’t have a single correct answer, so letting the model generate is fine. The problem is a product that hands the user one exact value (a number, a conversion or a dose) and expects them to act on it.

If you skip that question, here’s what happens.

When the model produces a number that should’ve come from a formula, nothing flags it. A wrong answer reads just like a right one, and our own research shows how common this is.