What does your model evaluation pipeline actually look like in production?
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
This is where people who run enterprise technology compare notes before a decision: what they’re evaluating, what they’d pick again and what went wrong. Most members are practitioners. Anyone who works for a vendor carries their company’s name on every post.
The bar is specifics over opinions: what you ran, at what scale and what you learned. Moderators check each new member’s first posts.
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
Blocking, allow-listing, DLP, enterprise licences, policy and training: what combination is working in practice?
Token spend, retrieval cost, caching, model routing. How are you measuring and reducing cost without hurting quality?