From AI Pilot to Production: A Practical Checklist
Most generative AI pilots never reach production. This checklist covers the data, evaluation, and guardrails that close the gap.
Generative AI demos are easy to build. Getting an AI feature into production, where real customers depend on it, takes more discipline. Here is the checklist we use.
1. Start with a measurable problem
Define the task and how you will measure success: deflected support tickets, time saved per document, or answer accuracy. Without a metric, you cannot tell if the model is improving.
2. Get your data ready
Retrieval-augmented generation (RAG) is only as good as the content it retrieves. Clean up duplicate and outdated documents, add metadata, and decide who is allowed to see what.
3. Build an evaluation set
Collect real questions and expected answers. Run every prompt or model change against this set so you catch regressions before users do.
4. Add guardrails
- Restrict the assistant to approved sources and tools.
- Validate inputs and outputs, especially before taking actions.
- Keep a human in the loop for high-impact decisions.
5. Monitor cost, latency, and quality
Log every interaction, track token usage, and review a sample of conversations each week. Small prompt or retrieval changes can have large effects.
Treat AI features like any other production system — tested, observed, and continuously improved — and they will earn your users' trust.