Andrew Ng: AI engineering tactics must calibrate to project stage, from evals to architecture
DeepLearningAI · x · 2026-09-26
In The Batch, Andrew Ng argues one of the hardest AI engineering skills is calibrating tactics to project stage: early 0-to-1 projects can get by with a dozen manually reviewed examples and casual architecture, while mature products need tens of thousands of test cases, detailed rubrics, and rigorous downstream-effect evaluation. The issue also covers Claude Opus 5.5 metrics, the viral Jev classification model, Devin Fusion's lead-and-sidekick models in one harness, and message passing for decentralized agents.
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