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Vanishing Gradients

Episode 29: Lessons from a Year of Building with LLMs (Part 1)

Jun 26, 2024
Experts from Amazon, Hex, Modal, Parlance Labs, and UC Berkeley share lessons learned from working with Large Language Models. They discuss the importance of evaluation and monitoring in LLM applications, data literacy in AI, the fine-tuning dilemma, real-world insights, and the evolving roles of data scientists and AI engineers.
01:30:21

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Quick takeaways

  • Evaluation and monitoring are crucial in LLM applications, data literacy is vital in AI, balancing fine-tuning is key.
  • Real-world lessons from LLM building, the evolving role of data scientists in AI, and the importance of optimized prompts.

Deep dives

Interest in Large Language Models

Developing a fascination for large language models stemmed from encountering the advancements in natural language processing technologies and realizing their potential impact on data-driven decision-making. Brian's career transition from data science to machine learning was guided by this breakthrough, prompting him to delve deeper into AI applications. Similarly, Charles's neuroscience background prompted a shift towards AI to fulfill the quest of understanding intelligence through computational means, aligning with Shreya's aspiration for intelligent software.

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