This is a sample draft shared ahead of publication. Content may change before it is finalized.
A proof of concept answers one question: can this work at all? Production asks many more. Will it work reliably for every user, on messy real-world inputs, at an acceptable cost, while meeting security and compliance requirements? Teams that treat the move to production as a simple scale-up tend to be surprised by how much remains to be done.
Define success before you build
Prototypes are often judged by impressions. Production systems need explicit success criteria: what tasks the system must handle, what quality is acceptable, and what a failure looks like. Writing these down early makes it possible to measure progress and to decide when the system is ready.
Invest in evaluation early
An evaluation set built from realistic examples is the most valuable asset in an AI project. It turns subjective debate into measurable comparison and lets you change prompts, models, or retrieval settings with confidence.
- Collect representative inputs, including difficult and edge cases
- Define expected outputs or grading criteria for each example
- Automate the evaluation so it runs on every significant change
- Track results over time to catch regressions
Treat data as a product
Prototype data is usually a clean snapshot. Production data changes, arrives late, and contains surprises. Pipelines need validation, ownership, and monitoring — especially for retrieval systems where stale or missing documents directly affect answer quality.
Design the integration, not just the model
Value comes from fitting AI into real workflows: the right inputs at the right time, outputs people can act on, and clear paths for review when confidence is low. Authentication, permissions, logging, and error handling deserve the same attention as model choice.
Plan for operations and ownership
After launch, someone must watch quality, cost, and latency, respond to feedback, and keep up with model changes. Deciding who owns the system — and giving them the tools to do it — is what keeps an AI capability valuable over time.
