Evaluating RAG for large scale codebases
qodo.ai
qodo.ai
"LLMs as a judge" is more about addressing the failure mode of auto-regressive (one-token at a time) generation letting an LLM lead itself astray due to its previous choices, rather than telling you any general truth.
Finally I'd note that in every maths challenge I ever completed as a student, you were strongly advised to go back and check your own work at the end if you had time left over, and for me this usually led to me catching things I'd missed the first time.
It seems their pr is willing to make much stronger claims than you will.
The problem of matching an answer to a given answer is a lot simpler than generating an answer. Especially for an LLM which has language transformation as one of its core competences.
It's student writing judge evaluation questions for himself as judge, judging judge (himself) and evaluating himself later using own judgement (as judge).
1 student all the way down.
If it thinks eating concrete makes you stronger, it's going to think that and give green light end to end.
It sounds superfluous, but it works very well as it saves time and frustration by allowing you to quickly fix the stuff that wouldn't pass review anyway.
The LLM as a judge-concept works in a similar way. Instead of "give a good answer", the task and perspective is "does this make sense?", which is very different.