How big should my golden dataset be?

Start with 50–200 carefully curated examples per user journey. Tag cohorts (PII, tool use, RAG, edge cases). Keep it maintainable and rotate 10–20% monthly to avoid overfitting.

What metrics should gate my AI releases?

Minimum pass rate on rubric/accuracy (e.g., 90%), p95 latency under your SLO (e.g., 1.2s), hallucination rate below threshold (e.g., <5%), and schema validity at ~100%. Tune based on business impact.

Do I need LangSmith/Weights & Biases, or is promptfoo enough?

Start with promptfoo in CI for go/no-go. Add LangSmith or W&B when you want deeper trace-level analysis, error clustering, and longitudinal monitoring across datasets.

How do I prevent prompt edits from bypassing gates?

Store prompt manifests in git, block direct edits in admin UIs, require PRs, and make ArgoCD sync depend on the eval artifact for that manifest’s version.

How do I control cost as traffic scales?

Introduce a Redis semantic cache, clip context and max tokens, pick smaller models for low-risk paths, and rate-limit expensive tools. Alert on `cost_usd_per_request` and `tokens_total`.

Ai-delivery · Nov 17, 2025 · 9 minute read

The Prompt Drift That Tanked Conversions: Versioned Prompts, Golden Datasets, and Automatic Regression Gates

Q: Do I need LangSmith/Weights & Biases, or is promptfoo enough?

Start with promptfoo in CI for go/no-go. Add LangSmith or W&B when you want deeper trace-level analysis, error clustering, and longitudinal monitoring across datasets.

Q: How do I prevent prompt edits from bypassing gates?

Store prompt manifests in git, block direct edits in admin UIs, require PRs, and make ArgoCD sync depend on the eval artifact for that manifest’s version.

Q: How do I control cost as traffic scales?

Introduce a Redis semantic cache, clip context and max tokens, pick smaller models for low-risk paths, and rate-limit expensive tools. Alert on `cost_usd_per_request` and `tokens_total`.

If your AI features keep “mysteriously” changing behavior in prod, you don’t have a model problem—you have an engineering problem. Version the prompts, version the data, and put a gate in front of every release.

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The Prompt Drift That Tanked Conversions: Versioned Prompts, Golden Datasets, and Automatic Regression Gates

Key takeaways

Implementation checklist