AI Product Engineering: Shipping LLM Features That Last
For product-minded engineers and tech leads who must decide what to build with AI and keep it working after launch. You will scope, build, evaluate, extend and operate one real LLM feature, a support assistant for a food-delivery app, and leave with the prompt contract, eval set, regression gate and launch checklist to reuse on your own.
Course Content
See the finished feature first, learn why a working demo is not yet a feature, and get the five-stage lifecycle the rest of the course follows.
Decide whether AI is worth it, draw the lines the feature must never cross, and design a product that stays useful when the model is unsure or wrong.
Turn a vague prompt into a contract with fixed inputs, outputs, rules and refusals, make the output something code can check, and release prompt changes the way you release code.
Build a 120-ticket eval set from real traffic, grade outputs with code, rubrics and a carefully checked model judge, turn failures into a taxonomy, and gate every release on it.
Match each measured failure to the capability that fixes it, then add retrieval, tools, fine-tuning or a different model only where the evidence says they pay for themselves.
Keep the feature safe, available and measurably good after launch: limit what a fooled model can do, degrade gracefully when the provider fails, watch the right numbers, and run a launch review you can reuse.