Run live A/B tests on your prompts and models, version prompts outside your codebase, and get a plain-English verdict backed by real statistics — LLM evaluation with proof, not gut feeling. https://llmjury.com/blog 2026-07-16T12:00:00.000Z LLMJury https://llmjury.com/blog/offline-evals-are-not-enough 2026-07-16T12:00:00.000Z 2026-07-16T12:00:00.000Z

Golden datasets are unit tests, not proof. Distribution drift, eval overfitting, and the metrics that only exist in production — latency, cost, and what users do next.

https://llmjury.com/blog/why-ab-test-prompts 2026-07-16T12:00:00.000Z 2026-07-16T12:00:00.000Z

Ten playground outputs can’t tell you what a prompt change does to real traffic. The case for measuring every prompt edit the way you measure every other production change.

https://llmjury.com/blog/announcing-llmjury 2026-07-01T12:00:00.000Z 2026-07-01T12:00:00.000Z

Why “it feels better” is not an eval strategy, and how SRM gates, FDR correction, and an LLM judge make prompt experiments trustworthy.