I tried asking LLMs about food before. They all say "I can't tell for certain, but this is an estimate based on the ingredients I can spot/infer/guess".

You need to write a specific prompt to avoid any warnings.

Of course a lot of people don't know what limitations LLMs have, so there's some value to a blog post about it, but it's not as black-and-white as the article might suggest with its graphs.

The prompt (documented here: https://www.diabettech.com/wp-content/uploads/2026/04/Supple...) lists specific instructions and a specific output format that doesn't allow the LLM any room for explanation or warning in processable data (only in notes fields). In fact, the prompt explicitly tells the LLM to ignore visual inferencing for some statistics and to rely on a nutrition authority instead.

Even in that intentionally restricted format, the English language output uses words like "roughly" and "estimated" in the LLMs I've tested.

Sure, if you take the numeric values and plot them in graphs, you get wildly inconsistent results, but that research method intentionally restricts the usefulness and reliability of the LLMs being researched.

What's much more troubling is this line from the preprint:

> The open-source iAPS automated insulin delivery (AID) system now offers food analysis through APIs from OpenAI, Anthropic and Google [8]

The linked app does seem to have a disclaimer, though:

> "AI nutritional estimates are approximations only. Always consult with your healthcare provider for medical decisions. Verify nutritional information whenever possible. Use at your own risk."

> Then the correct answer is “I can’t tell.”

From the paper they're using structured JSON schema mode opposed to freeform answers, so it can't. Models do typically caveat their answer for questions like this, in my experience.

LLMs had no agency to choose such a course of action.

They’re algorithms and they were designed this way.