Eugene Yan on Using LLMs as Judges: Insights, Challenges, and Best Practices

In this episode, Eugene discusses a groundbreaking article on using Large Language Models (LLMs) as judges, exploring their application, potential, and challenges. Eugene and Hamel delve into the usefulness of literature, integrating research, and performing experiments with LLMs. They also share their experiences and insights on fine-tuning models, incorporating chain of thought prompts, and dealing with human alignment. Additionally, the discussion covers practical issues in data labeling, criteria development, and leveraging advanced tools like DSPY to streamline the prompting process. Tune in to gain deep insights into the world of LLM evaluations and how to maximize their effectiveness in applied research contexts. 00:00 Introduction to Using LLM as a Judge 00:14 The Role of Literature in Research 00:35 Eugene's Process and Insights 02:20 Skepticism and Re-evaluation 05:21 Chain of Thought and Performance 12:54 Fine-Tuning and Structured Output 18:33 Introduction to React Apps and Artifacts 19:04 Using Framer with Artifacts 19:36 Evaluating Language Models (LLMs) 22:13 Challenges in Data Labeling 24:15 Writing Effective Criteria 35:21 The Importance of Prompting 38:36 Conclusion and Call for Feedback