What metrics should gate my AI deploys?

For RAG: faithfulness, context precision/recall, p95 latency, timeout rate, and cost per request. For extractive/structured tasks: exact match/F1 and schema validity rate. Set thresholds vs. a baseline and fail the pipeline on regressions.

How do I version prompts and datasets without boiling the ocean?

Start with Git for prompts (semver tags), DVC or Delta Lake for dataset snapshots, and embed prompt_version/dataset_hash/index_id in every log. Evolve to a metadata store later if needed.

How do I detect hallucinations in production?

Use an eval stream (goldens) for a ‘truth’ signal and sample live traffic for self-checks (e.g., confidence from a secondary verifier model). Track answer_changed_rate on stable inputs as an early warning.

What guardrails actually reduce incidents?

Schema validation with re-ask, content policy enforcement, circuit breakers (Istio/Envoy), and latency-aware retries with jitter. Combined with canaries/feature flags, these cut MTTR and incident volume significantly.

Can I do this with closed providers like OpenAI and Anthropic?

Yes. Treat providers as pluggable. Stamp lineage, measure tokens/latency, and gate deploys with evals. Keep at least one backup model/provider and a cached fallback.

Ai-delivery · Oct 27, 2025 · 8 minute read

The Friday Prompt Change That Tanked Conversions (And How We Stopped It Happening Again)

Stabilize AI behavior with versioned prompts, datasets, and automated regression barriers. Instrument everything so drift doesn’t blindside your roadmap.

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The Friday Prompt Change That Tanked Conversions (And How We Stopped It Happening Again)

Key takeaways

Implementation checklist