From Prototype to Production: The AI Delivery Checklist
A practical, step-by-step checklist for taking AI features from demo to production with confidence.
Great AI demos are easy. Production-grade AI is not. The gap is almost always operational: data quality, evaluation, latency, safety, and monitoring are the real blockers, not the model.Start with a clear product outcome and define success metrics before you build. This includes success criteria for users, guardrails for unacceptable behavior, and a baseline you can compare against after launch.
Data readiness is the first gate: establish data lineage, validate coverage for key user segments, and define a refresh cadence. Without this, evaluation results will look good in the lab and fail in the wild.
Next, build an evaluation stack that combines offline tests, scenario-based reviews, and lightweight human review. Make evaluation repeatable so you can run it on every model or prompt change.
Treat latency and cost like first-class requirements. Set budgets and design fallbacks early so the feature remains usable under load, during partial outages, or when cost spikes.
Finally, monitor post-launch behavior: drift, outliers, safety incidents, and user trust signals. If you cannot observe it, you cannot improve it. Production AI is a feedback loop, not a one-time release.
Key takeaways
- Define success metrics and guardrails before building.
- Make evaluation repeatable and part of release gating.
- Treat latency, cost, and safety as product requirements.
- Build monitoring for drift and trust signals from day one.
Checklist
- Data coverage and freshness confirmed
- Evaluation suite with baseline results
- Latency and cost budgets documented
- Safety guardrails and fallbacks implemented
- Post-launch monitoring and alerts configured
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