How big should my golden dataset be?

Start with 50–100 representative cases per feature, including edge cases and safety probes. Grow to 500+ as you see failure modes. The key is coverage and stability, not size alone.

Do I need a separate CI pipeline for prompts?

Yes. Treat prompts as artifacts: lint, unit-check for schema, run evals against golden sets, then publish a versioned artifact. We typically wire this into GitHub Actions and gate merges on eval metrics.

What if my vendor silently updates the model?

Pin model IDs and log `model` and `api_version` in spans. Run daily canary evals. If metrics drift, auto-fallback to a known-good model or earlier prompt version, and page the on-call.

How do I measure hallucination rate reliably?

For RAG, require citations and auto-check that answers quote retrieved docs. Use rubric scoring with `promptfoo`/`LangSmith` plus spot human labeling for high-risk categories.

Won’t all this slow my team down?

It speeds you up. Once the pipelines and gates are in, changes ship faster because risk is quantified. We’ve seen teams cut MTTR by >50% and ship prompt changes multiple times a day safely.

Ai-delivery · Nov 1, 2025 · 10 minute read

Stop Shipping Prompt Drift: Versioned Prompts, Golden Datasets, and Regression Barriers That Hold the Line

If you can’t version it, test it, and gate it, you’re gambling with production. Here’s the playbook we use to freeze prompt/feature drift before it costs you customers and cash.

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Stop Shipping Prompt Drift: Versioned Prompts, Golden Datasets, and Regression Barriers That Hold the Line

Key takeaways

Implementation checklist