It feels like with LLMs I develop a prompt and then get some kind of output that's not very well structured and requires some kind of human oversight to verify that the result I've gotten back is of quality, and the non-deterministic nature of LLMs is the main reason for this.
I see solutions using LLMs, VLMs, etc that will achieve things that have been solved before (things like OCR, parsing, or even just code generation). They do these problems really well and really fast... 90% of the time. How do I get a scaffolding setup so I can be 100% confident in my solution (meaning 100% confident that I know what it's going to do)?
With standard ML, I have things like confidence scores to base whether or not I accept an output as valuable. And generally speaking, it's pure (i.e., given the same input I will always get the same output). With LLMs and the like, it feels like this piece is missing.
Comments URL: [https://news.ycombinator.com/item?id=49293943](https://news.ycombinator.com/item?id=49293943)
Points: 1