You need to know if your LLM lies to your users behind your back. So you appoint another LLM to keep an eye on it. The classic LLM-as-a-judge. But it's a dangerous, costly game to play. First of all, it doesn't separate detection with handling. It's far from being consistent. And it bills you a second model call every single time! So how about getting ourselves a more trustworthy, efficient, empiric evaluator? Predictive entropy hands us a tempting method for free: using token logprobs variance. But it can cheerfully wave through a confident lie, and might panic over a harmless, creative truth. So an even better solution would be using *semantic* entropy. It resamples the answer, clusters by meaning, and measures the disagreement. Stronger. But now you're paying for N generations. No free lunch. This session is about that fascinating tradeoff: Cost x subjectivity x reliability. Which method should you reach for and when? Closes with an open-source to play with all three.
AI Solutions Architect