Lenny's Podcast: Product | Career | Growth

Al Engineering 101 with Chip Huyen (Nvidia, Stanford, Netflix)

588 snips
Oct 23, 2025
Chip Huyen, a prominent AI engineer and educator with experience at NVIDIA, Stanford, and Netflix, dives into the nuances that make AI applications successful. She explains the critical differences between pre-training and post-training, highlighting that fine-tuning should be a last resort. Chip also discusses the significance of data quality over database choice and how reinforcement learning from human feedback operates. She emphasizes that many AI challenges stem from UX issues and shares insights on how organizations can effectively adopt AI tools.
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ADVICE

Prioritize Users Over Hype

  • Talk to users and iterate on feedback instead of chasing the latest AI hype.
  • Focus on data, prompts, UX, and reliability to improve AI apps more than swapping models.
INSIGHT

Pre-Training vs Post-Training

  • Pre-training builds broad language statistics while post-training tailors behavior for tasks.
  • Fine-tuning is post-training and should be used when task-specific adjustments are necessary.
INSIGHT

How RLHF Improves Model Behavior

  • RLHF trains models by using human comparisons to build a reward model and optimize outputs.
  • You can replace human labels with AI or verifiable signals, but the mechanism remains rewarding better responses.
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