Demos are easy; production is hard. Reliable AI features require the same engineering rigor as any other critical system — plus a few new disciplines.
Constrain the problem
Narrow, well-scoped tasks are far more reliable than open-ended ones. Define exactly what the model should and shouldn't do, and design guardrails around it.
Evaluate continuously
Build evaluation datasets and run them on every change. Without evals, you're flying blind every time you tweak a prompt or swap a model.
Plan for failure
Models will produce wrong answers. Design fallbacks, human-in-the-loop checks, and clear error states so failures degrade gracefully.
Daniel Osei
CTO, Ofstech
Daniel writes about building and scaling great software teams at Ofstech.
