Andrew Ng breaks down AI engineering skills: evaluation is the key differentiator
AI寒武纪 · wechat · 2026-08-22
Andrew Ng published an article breaking down the core capabilities of building AI applications into six key areas, emphasizing that AI engineering is essentially about decision-making under uncertainty.
Core Competencies
- LLM Basics: Understanding tokenization, inference compute control, multimodal selection, and decisions on fine-tuning vs. deployment.
- Data Engineering: Going beyond RAG to include knowledge graphs, semantic layers, and continuous data quality maintenance.
- Agent Systems: Designing workflows (serial/parallel), tool use, memory management, and safety boundaries.
- Evaluation-Driven Development: The key differentiator. Establishing a rigorous evaluation loop to guide iterations with data rather than intuition, aligning metrics with business goals.
- Production Ops: Focusing on observability (drift monitoring), regression testing (statistical methods), and cost/latency control.
- ML Foundations: Using ML concepts like bias-variance tradeoff and error analysis as a mental framework for better decision-making.
Related event: Andrew Ng Unveils AI Engineering Skills Map(4 posts)→
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