Andrew Ng: rigid testing on early-stage AI projects is a recipe for stall
DeepLearningAI · x · 2026-09-26
Andrew Ng's latest letter in The Batch argues that calibrating tactics to project stage is one of the hardest yet most important AI engineering skills. Imposing mature-product rigor on 0-to-1 projects causes them to stall.
Using an automated customer-service email system as an example:
- Early stage: manually review a dozen examples for sanity
- Growth stage: hundreds of test examples plus a written rubric
- Mature product: tens of thousands of examples, detailed rubrics, and evaluation of downstream effects like customer retention
The letter also covers how to scale eval pipelines, choose software architecture, and structure product feedback loops. Over-designing early or under-designing late are both common failure modes.
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