[Netcob]

I don't think LLMs are mature enough as a technology to be blindly used as a dependency, and they might never be.

The big question is, how do you train LLMs that are useful to both humans and services while not embarrassing the company that trained them?

LLMs are pretty good at translating - but if they don't like what they're reading, they simply won't tell you what it says. Which is pretty crazy.

LLMs are pretty good at extracting data and formatting the results as JSON - unless they find the data objectionable, then they'll basically complain to the deserializer. I have to admit that's a little bit funny.

Right now, if you want to build a service and expect any sort of predictability and stability, I think you have to go with some solution that lets you run open-weights models. Some have been de-censored by volunteers, and if you find one that works for you, you can ignore future "upgrades" until you find one that doesn't break anything.

And for that it's really important to write your own tests/benchmarks. Technically the same goes for the big closed LLM services too, but when all of them fail your tests, what will you do?

[the8472]

I can get gore and porn results on google image search, I just have to select SafeSearch: Off. Can't they do the same for AIs?