Evals are critical, and I love the practicality of this guide!
One problem not covered here is: knowing which data to review.
If your AI system produces say 95% accurate responses, your Evals team will spend too much time reviewing production logs to discover different AI failure modes.
To enable your Evals team to only spend time reviewing the high-signal responses that are likely incorrect, I built a tool that automatically surfaces the least trustworthy LLM responses:
One problem not covered here is: knowing which data to review.
If your AI system produces say 95% accurate responses, your Evals team will spend too much time reviewing production logs to discover different AI failure modes.
To enable your Evals team to only spend time reviewing the high-signal responses that are likely incorrect, I built a tool that automatically surfaces the least trustworthy LLM responses:
https://help.cleanlab.ai/tlm/
Hope you find it useful, I made sure it works out-of-the-box with zero-configuration required!