[RasmusFromDK]

Yeah it is to some degree. I tried to use it as much as possible, but there's always those annoying edge cases that makes me not trust the results and I have to check everything, and it ended up being faster just building some simple UI where I can easily classify the name myself.

Part of the problem is simply due to bad data from the websites. Just as an example - there's a 2-week contact lens called "Acuvue Oasys". And there's a completely different 1-day contact lens called "Acuvue Oasys 1-Day". Some sites have been bad at writing this properly, so both variants may be called "Acuvue Oasys" (or close to it), and the way to distinguish them is to look at the image to see which actual lens they mean, look at the price etc.

It's true that this could probably also be handled by AI, but in the end, classifying the lenses takes like 1-2% of the time it takes to make a scraper for a website so I found it was not worth trying to build a very good LLM classifier for this.

[alexpotato]

> It's true that this could probably also be handled by AI, but in the end, classifying the lenses takes like 1-2% of the time it takes to make a scraper for a website so I found it was not worth trying to build a very good LLM classifier for this.

This is true for technology in general (in addition to specifically for LLMs).

In my experience, the 80/20 rule comes into play in that MOST of the edge cases can be handled by a couple lines of code or a regex. There is then this asymptotic curve where each additional line(s) of code handle a rarer and rarer edge case.

And, of course, I always seem to end up on project where even a small, rare edge case has some huge negative impact if it gets hit so you have to keep adding defensive code and/or build a catch all bucket that alerts you to the issue without crashing the entire system etc.