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

In this episode of Lenny's Podcast, Chip Huyen, an AI expert, discusses key aspects of building successful AI products. She differentiates between pre-training and post-training, emphasizing the importance of supervised fine-tuning and reinforcement learning. Chip highlights the significance of talking to users, preparing better data, and optimizing workflows over merely chasing the latest AI news. She also shares insights on the economics of data labeling, the role of evaluations in AI development, and the strategic adoption of AI tools within companies, noting the challenges in measuring productivity gains and the varying responses to AI assistive tools among different engineering teams. Furthermore, Chip predicts future trends in AI, including the blurring of lines between job functions, increased automation, and the rise of multimodal AI experiences.

Outlines

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Lenny's Podcast: Product | Career | Growth

The Disconnect in AI App Development: Hype vs. Reality

Understanding Pre-training, Post-training, and Fine-tuning in AI Models

Supervised Learning, Reinforcement Learning, and the Data Labeling Landscape

Evaluating AI Models: The Importance and Pragmatism of Evals

Retrieval Augmented Generation (RAG) and Data Preparation

AI Tool Adoption and Internal Productivity

AI's Impact on Engineering Teams and the Importance of System Thinking

AI Engineering vs. ML Engineering and Future Predictions

Test Time Compute and the Idea Crisis

Lightning Round: Book Recommendations, Life Mottos, and Creative Writing

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

Lenny's Podcast: Product | Career | Growth

00:00The Disconnect in AI App Development: Hype vs. Reality

The Disconnect in AI App Development: Hype vs. Reality

07:34Understanding Pre-training, Post-training, and Fine-tuning in AI Models

Understanding Pre-training, Post-training, and Fine-tuning in AI Models

15:21Supervised Learning, Reinforcement Learning, and the Data Labeling Landscape

Supervised Learning, Reinforcement Learning, and the Data Labeling Landscape

22:24Evaluating AI Models: The Importance and Pragmatism of Evals

Evaluating AI Models: The Importance and Pragmatism of Evals

31:55Retrieval Augmented Generation (RAG) and Data Preparation

Retrieval Augmented Generation (RAG) and Data Preparation

38:51AI Tool Adoption and Internal Productivity

AI Tool Adoption and Internal Productivity

45:21AI's Impact on Engineering Teams and the Importance of System Thinking

AI's Impact on Engineering Teams and the Importance of System Thinking

55:04AI Engineering vs. ML Engineering and Future Predictions

AI Engineering vs. ML Engineering and Future Predictions

1:04:16Test Time Compute and the Idea Crisis

Test Time Compute and the Idea Crisis

1:11:40Lightning Round: Book Recommendations, Life Mottos, and Creative Writing

Lightning Round: Book Recommendations, Life Mottos, and Creative Writing