How big should my golden dataset be?

Start with 200–500 examples that cover your top intents and edge cases. Grow it as you see real incidents; each incident becomes a new test. Version it with DVC/LakeFS so changes are auditable.

Do I need a separate model per feature?

Not necessarily. You need a separate contract per feature: prompt, parameters, tools, and evals. Multiple features can share a base model, but treat each feature’s contract and dataset independently.

What if providers change behavior without notice?

This is common. Your regression barrier will catch it in CI if you pin `api-version` and re-run evals regularly. In production, watch for P95/token drift and trip a circuit breaker to a known-good version if thresholds are exceeded.

Is this overkill for early-stage teams?

If you’re shipping to real users, it’s cheaper than incidents. Start small: one modelspec, one dataset, one barrier. You can add sophistication as volume grows.

Ai-delivery · Oct 1, 2025 · 9 minute read

The 2 a.m. Prompt Tweak That Nuked Your Conversion (And How to Stop It Happening Again)

Stabilize AI systems with versioned prompts, eval datasets, and automated regression barriers—plus the observability and guardrails that keep you out of incident review hell.

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The 2 a.m. Prompt Tweak That Nuked Your Conversion (And How to Stop It Happening Again)

Key takeaways

Implementation checklist