Andrew Ng just released a 75-minute Stanford lecture on full AI Engineering. How to go from chasing perfect prompts to building systems that can improve themselves: 0% → 00:05 - see why some AI teams move 10X faster 20% → 07:12 - choose an architecture and ship faster 40% → 30:12 - avoid the 97% accuracy trap 60% → 48:12 - run the build → error analysis → retrain loop 80% → 57:55 - build deep-research agents 100% → 1:06:30 - find bottlenecks inside agent pipelines Most people are still trying to write the perfect prompt. Andrew Ng is teaching the entire stack: Architecture → Evals → Loops → Agents → Graphs Prompt engineering is the old workflow. Building systems that find and fix their own bottlenecks is the next one. This 75-minute Stanford lecture is worth more than most paid AI engineering courses. Bookmark it and watch today. Then read the full agent engineering roadmap below ↓