Ai Evals
Three Signs of Effective AI Evals and Five Lessons for Engineering Production-Grade Systems
Effective AI evaluations enable rapid model integration within 24 hours of release, seamless incorporation of user feedback, and proactive assessment of new use cases before shipping. Evals demand rigorous engineering of datasets reconciled with real-world usage, custom scorers tailored as product s…
Hazing: Fuzz Testing Solves AI's Last-Mile Reliability Crisis via Iterative Optimization
Haze addresses AI's core brittleness—Lipshitz discontinuity where minor input perturbations cause wildly divergent outputs—by fuzz testing through large-scale iterative optimization, searching inputs to expose failures before production. Judging outputs scales compute via agentic frameworks like Ver…
Brain Trust's Loop Agent Automates AI Evals by Leveraging Frontier LLMs for Prompt, Data, and Scorer Optimization
Brain Trust's Loop is an agent integrated into their platform that automates optimization of prompts, datasets, and scorers using evals run on frontier models. Claude 4 achieves 6x better performance than prior leading models in improving prompts, datasets, and scorers, marking a breakthrough. Loop …
Benchmarking LLM Reasoning via Incomplete Information in Poker Tournaments
The LLM Poker Arena benchmarks frontier models' ability to handle probabilistic reasoning and opponent modeling under incomplete information via Texas Hold'em tournaments. Initial results from five trials show Claude Opus 4.5 leading in wins, though the sample size is currently too small for definit…