Originally published on AI Tech Connect.
What you'll set up, in one paragraph By the end of this guide you will have a repeatable way to answer one question that offline evals cannot: did this change to your LLM feature actually make things better for real people? You will define an Overall Evaluation Criterion — one primary success metric plus a small set of guardrails — assign users to variants with deterministic hashing, protect the experiment with a Sample Ratio Mismatch check, avoid the peeking trap with either a fixed horizon or a sequential test, reach for interleaving when you are changing retrieval, and finally graduate a trusted metric into a contextual bandit that routes traffic automatically. The theme throughout is simple: offline evals prove an output moved; only a live experiment proves it helped. Offline tells…
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