AI Product Engineering: Ship Reliable LLM Features End-to-End

AI product engineering involves identifying where AI adds value, measuring whether the feature performs as intended, selecting the capabilities the feature requires, and keeping it reliable in production. In this course, you’ll develop the practical judgment needed to make these engineering decisions. I developed this course based on my work in adaptive AI, neural networks, intelligent computing, and intelligent tutoring systems. As a published researcher and VP of Technical Content at Educative, I’ve found that the main challenge in AI product engineering is rarely the model itself. The harder work is building evaluation systems, safeguards, and decision frameworks around the model so the feature behaves reliably in production. Using an AI-powered support-ticket summarizer, you’ll learn to scope AI features, define prompts as structured contracts, build evaluation sets, diagnose failures, and decide when to use retrieval, tool calling, or fine-tuning. You’ll then harden the feature against misuse, runtime failures, and quality drift, using examples based on Perplexity, GitHub Copilot, and Cursor. By the end, you’ll have a repeatable AI product engineering framework for taking a feature from idea to a reliable production system, along with the skills to build AI features that remain reliable in production.