Generative Benchmarking with Kelly Hong - #728
In this episode, Kelly Hong, a researcher at Chroma, joins us to discuss "Generative Benchmarking," a novel approach to evaluating retrieval systems, like RAG applications, using synthetic data. Kelly explains how traditional benchmarks like MTEB fail to represent real-world query patterns and how embedding models that perform well on public benchmarks often underperform in production. The conversation explores the two-step process of Generative Benchmarking: filtering documents to focus on relevant content and generating queries that mimic actual user behavior. Kelly shares insights from applying this approach to Weights & Biases' technical support bot, revealing how domain-specific evaluation provides more accurate assessments of embedding model performance. We also discuss the importance of aligning LLM judges with human preferences, the impact of chunking strategies on retrieval effectiveness, and how production queries differ from benchmark queries in ambiguity and style. Throughout the episode, Kelly emphasizes the need for systematic evaluation approaches that go beyond "vibe checks" to help developers build more effective RAG applications. The complete show notes for this episode can be found at https://twimlai.com/go/728.

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Simplifying On-Device AI for Developers with Siddhika Nevrekar - #697

Simplifying On-Device AI for Developers with Siddhika Nevrekar - #697

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Genie: Generative Interactive Environments with Ashley Edwards - #696

Genie: Generative Interactive Environments with Ashley Edwards - #696

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Bridging the Sim2real Gap in Robotics with Marius Memmel - #695

Today, we're joined by Marius Memmel, a PhD student at the University of Washington, to discuss his research on sim-to-real transfer approaches for developing autonomous robotic agents in unstructured...

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Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - #694

Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - #694

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Mamba, Mamba-2 and Post-Transformer Architectures for Generative AI with Albert Gu - #693

Today, we're joined by Albert Gu, assistant professor at Carnegie Mellon University, to discuss his research on post-transformer architectures for multi-modal foundation models, with a focus on state-...

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Decoding Animal Behavior to Train Robots with EgoPet with Amir Bar - #692

Decoding Animal Behavior to Train Robots with EgoPet with Amir Bar - #692

Today, we're joined by Amir Bar, a PhD candidate at Tel Aviv University and UC Berkeley to discuss his research on visual-based learning, including his recent paper, “EgoPet: Egomotion and Interaction...

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How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird - #691

How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird - #691

Today, we're joined by Sarah Bird, chief product officer of responsible AI at Microsoft. We discuss the testing and evaluation techniques Microsoft applies to ensure safe deployment and use of generat...

1 Juli 202457min

Long Context Language Models and their Biological Applications with Eric Nguyen - #690

Long Context Language Models and their Biological Applications with Eric Nguyen - #690

Today, we're joined by Eric Nguyen, PhD student at Stanford University. In our conversation, we explore his research on long context foundation models and their application to biology particularly Hye...

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