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Contrarian Guide to AI: Jason Liu on Betting Against Agents while Doubling Down on RAG & Fine-Tuning

High Agency: The Podcast for AI Builders

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Improving AI Systems Performance and Interpretability

The chapter explores strategies for measuring performance and interpretability in AI systems, emphasizing the value of evaluating intermediate steps, relying on expert evaluation of data, and enhancing decision-making through structured processes. It discusses automating white-collar knowledge work, improving data visualization, and aligning metrics with business outcomes. The conversation also covers the benefits of large language models, enhancing recommendation systems, and the importance of specialized routers in improving system performance.

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