I think ChatGPT is just bad at whatever domain you have expertise in.

Talk to it about something you don't know about, and you'll think it's really good technology ;)

As an electronics engineer, I have tried it for such tasks, without success. I specified requirements (only the key/rarer ones, typically 1 or 2) and asked it to find components. It failed miserably, typically just insisting that some related but much more common component satisfied the requirement. More and more apologetic as I tired to guide/coax it along. I know that there are a few components available that satisfy the requirement, as well as several hundred that do not. And I know that the information is in digitally readable PDF files (as opposed to scans).

This specific failure might be s kind of averaging problem, where common answers around the general theme are preferred over more specific (and correct). LLMs can also fail completely at trivial concepts such as negation, or separating between "Y above X" and "Y below X".

This applies to many fields. It will come up with plausible looking but wrong answers and keeps apologising if you correct or point out the mistakes.

I've seen it with statistics as well, asking it to implement some things in code. You'll get working but mathematically wrong code.

ChatGPT can be hilariously bad at less common things, for example, I asked it for the uses of polyurethane foam in my native language, and it suggested it would be great for decorating cakes.

The thing about data sheets is you have to watch out for your own assumptions when reading them. If it doesn't explicitly say it'll do X... it won't, no matter how common it is in other parts of the same type.

It might help but you have to be the backstop when it comes to the final call. Measuring the false positive/false negative rate could be tedious, but it's important to have a good estimate of, in order to use it wisely.

How much would you pay for this feature?

Is typing your requirements that much easier than going through traditional search filters at Digikey?

I asked it all sorts of specifics about how to use my esp32 and it does surprisingly well

Do in context learning. Gather a huge sheet of specs for components you want to use, put it at the top of your chat, and then ask questions.

Probably because it isn't really built for that. It's a word soup generator, and not a technical database.

For this kind of task, you probably want a model that has specifically been trained on every product datasheet ever, and not ten million reddit threads and forum posts about how a 555 or 328p can solve any problem.

I doubt that chatgpt has been fed every datasheet for every part made in the last decade or two. Even if it had, that's likely far outweighed by the amout of noise coming from people talking about the most common parts.

But fundamentally I'm not sure that LLMs are great for this type of work. No two datasheets are the same and I've never seen one that wasn't missing some kind of information. What you very much do not want is an LLM hallucinating a value that does not actually exist in the datasheet. Or have it conflate two parts and mix up their values. These models just don't seem to be up to the task of returning real information from abstract queries. They're just meant to generate probabilistic text sequences.

Have you tried ChatGPT apps specialized for electronics, such as https://chatgpt.com/g/g-6PTe1fb3X-electronics-and-circuit-an... ?