Engineering insight
From AI demo to product feature: a production integration checklist
What changes when an LLM or ML experiment becomes a real feature inside a production product.
Start with the workflow
Define the user or operational outcome before choosing a model. AI should fit into an existing flow rather than become a separate novelty screen.
Make outputs predictable
Use structured schemas, validation and fallbacks so downstream code is not forced to interpret arbitrary model output.
Design for failure
Timeouts, provider errors, malformed responses and cost limits need explicit behavior. A production feature must degrade gracefully.
Measure the right thing
Track quality, latency, token/model cost and the business action that follows the output—not only whether the API returned 200.
Keep humans in the loop where needed
For high-impact actions, create review and override paths instead of treating model output as unquestionable truth.