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Comment by TeeWEE | Hacker News Reader

Basically it’s AI on top of AI for metadata extraction.

There are a lot of claims in the article but not a lot of hard data. In the end they still don’t know if the data is correct.

Good luck with your glutes allergy.

The weird thing for me is the prompt optimization loop? Why not fine tune the model instead of AI generating the prompt?

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Also, "healthy" as a boolean flag is, franky, a bit of a joke.

I see why you're confused. Some of these tags carry nutritional information, so it's easy to assume they all do.

The healthy tag is about marketing. It's a product positioning statement.

I'm guessing that if it is a real tag, then consumers have a "know it when I see it" feeling for certain kinds of food that they'd describe as "healthy." As a word that could be consistently well-defined, it's garbage. But that doesn't mean it's not useful for real-world consumers to find what they believe they want.

If I search "healthy" in DoorDash, Taco Bell comes up in the top results

> The weird thing for me is the prompt optimization loop? Why not fine tune the model instead of AI generating the prompt?

Why is it weird to optimise the prompt? Whether you optimise the model is a separate issue.

If you use any closed models you can’t fine tune them, which is another reason for most but here they also fine tuned models.

I am taking from their hero image that something like a gluten allergy would have to be verified by the merchant, but I’m just guessing that’s true. Feeding failure cases into an AI-led prompt tuning agent to solve them seems prone to a lot of problems though.

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