Do I really need human evals?

Some, yes—at least to calibrate automated scoring. The goal isn’t a giant labeling operation. It’s a small, consistent rubric on a sampled slice so your automated metrics don’t drift into self-congratulation.

What’s the minimum viable evaluation harness?

CI gating on a golden set + adversarial set, OpenTelemetry traces with prompt/model versions, a feature-flag kill switch, schema validation, and alerts on p95 latency + safety violation rate + fallback rate.

How do I avoid logging sensitive data while still being able to debug?

Log versions, hashes, doc IDs, and policy outcomes; store raw payloads only in a restricted replay vault (if at all). Redact at the edge before logs, and treat eval artifacts as sensitive data.

How do I handle vendor model changes that break behavior?

Run shadow traffic against the current production prompt/model on a schedule and alert on eval deltas. Also pin model versions when possible, and keep prompts/configs versioned and deployable.

Ai-delivery · Jan 11, 2026 · 8 minute read

The Eval Harness That Stops Your LLM Feature From Gaslighting Users (Before, During, and After Release)

If you ship generative features without an evaluation harness wired into your CI/CD and observability stack, you’re not “moving fast.” You’re flying blind—with a bigger blast radius.

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The Eval Harness That Stops Your LLM Feature From Gaslighting Users (Before, During, and After Release)

Key takeaways

Implementation checklist