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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?
Sampling, tiered retention, OpenTelemetry pipelines, switching vendors? What cut the bill the most?