Comment by ReDeiPirati

Comment by ReDeiPirati 9 hours ago

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> We find testing and evals to be the hardest problem here. This is not entirely surprising, but the agentic nature makes it even harder. Unlike prompts, you cannot just do the evals in some external system because there’s too much you need to feed into it. This means you want to do evals based on observability data or instrumenting your actual test runs. So far none of the solutions we have tried have convinced us that they found the right approach here.

I'm curious about the solutions the op has tried so far here.

ramraj07 21 minutes ago

Its a 2 day project at best to create your own bespoke llm as judge e2e eval framework. Thats what we did. Works fine. Not great. Still need someone to write the evals though.

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ColinEberhardt 9 hours ago

Likewise. I have a nasty feeling that most AI agent deployments happen with nothing more than some cursory manual testing. Going with the ‘vibes’ (to coin an over used term in the industry).

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  • radarsat1 7 hours ago

    A lot of "generative" work is like this. While you can come up with benchmarks galore, at the end of the day how a model "feels" only seems to come out from actual usage. Just read /r/localllama for opinions on which models are "benchmaxed" as they put it. It seems to be common knowledge in the local LLM community that many models perform well on benchmarks but that doesn't always reflect how good they actually are.

    In my case I was until recently working on TTS and this was a huge barrier for us. We used all the common signal quality and MOS-simulation models that judged so called "naturalness" and "expressiveness" etc. But we found that none of these really helped us much in deciding when one model was better than another, or when a model was "good enough" for release. Our internal evaluations correlated poorly with them, and we even disagreed quite a bit within the team on the quality of output. This made hyperparameter tuning as well as commercial planning extremely difficult and we suffered greatly for it. (Notice my use of past tense here..)

    Having good metrics is just really key and I'm now at the point where I'd go as far as to say that if good metrics don't exist it's almost not even worth working on something. (Almost.)

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