Offline evals

• Before rolling out prompt or model updates.
• Benchmarking providers or retrieval tweaks.
• Testing edge cases that are rare in production.

Offline evals speed iteration and reduce risk, but they can mis-predict real UX if the dataset is biased. PMs must ensure data represent real intents and keep metrics aligned to business goals. They also influence time-to-ship by being part of the release gate.

Combine real anonymized traffic with targeted synthetic cases. Measure accuracy, refusals, toxicity, latency, and cost. In 2026, include grounding and PII leak checks automatically and track prediction vs. production gap to recalibrate.

An offline eval on 400 cases shows a new reranker lifts accuracy +6 points with +40 ms latency. The PM greenlights an A/B test, cutting live experimentation time by half.

• Relying solely on synthetic data, missing real user phrasing.
• Not recalibrating when production intent mix shifts.
• Ignoring safety checks because they are harder to label.