Atita Arora & Deanna Emery - Navigating RAG Optimization with an Evaluation-Driven Compass
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Retrieval Augmented Generation (RAG) has become a cornerstone for integrating domain-specific content and addressing hallucinations in AI applications. As the adoption of RAG solutions intensifies across industries, a pressing challenge emerges: understanding and identifying where within the complex RAG framework changes and improvements can be made. This talk delves into the methodology of extracting crucial indicators from your RAG pipeline, empowering informed decision-making during experimentation. Join us as we navigate an end-to-end process for RAG experimentation and evaluation, offering insights into optimizing performance and addressing hurdles along the RAG implementation journey.
Atita Arora
Atita Arora is a seasoned and esteemed professional in information retrieval systems and has decoded complex business challenges, pioneering innovative information retrieval solutions in her 15-year journey as a Solution Architect / Relevance strategist / Individual Contributor and works as Solution Architect at Qdrant. She has a robust background from her impactful contributions as a committer in various information retrieval projects. She has a keen interest in making revolutionary tech innovations accessible and implementable to solve real-world problems. She is currently actively researching about evaluating RAGs while navigating the world of vectors and LLMs, seeking to uncover insights that can enhance their practical applications and effectiveness.
Atita Arora
Atita Arora is a seasoned and esteemed professional in information retrieval systems and has decoded complex business challenges, pioneering innovative information retrieval solutions in her 15-year journey as a Solution Architect / Relevance strategist / Individual Contributor and works as Solution Architect at Qdrant. She has a robust background from her impactful contributions as a committer in various information retrieval projects. She has a keen interest in making revolutionary tech innovations accessible and implementable to solve real-world problems. She is currently actively researching about evaluating RAGs while navigating the world of vectors and LLMs, seeking to uncover insights that can enhance their practical applications and effectiveness.
Deanna Emery
Deanna is the Founding AI Researcher at Quotient AI, where she is leading research on evaluation of Large Language Models in real-world products and applications. Before Quotient, Deanna was a Principal Data Scientist at Aon, where she led the team building language models for valuation of intellectual property assets. She began her career as a researcher at Harvard-Smithsonian Center for Astrophysics and Caltech LIGO. Deanna has a MS in Machine Learning from UC Berkeley and BA in Physics from Harvard University. She is passionate about diversity and inclusion in STEM; she has conducted research on diversity in named patent inventors, working with companies to measure and address diversity gaps, and she is an active board member at a STEM education non-profit.
Deanna Emery
Deanna is the Founding AI Researcher at Quotient AI, where she is leading research on evaluation of Large Language Models in real-world products and applications. Before Quotient, Deanna was a Principal Data Scientist at Aon, where she led the team building language models for valuation of intellectual property assets. She began her career as a researcher at Harvard-Smithsonian Center for Astrophysics and Caltech LIGO. Deanna has a MS in Machine Learning from UC Berkeley and BA in Physics from Harvard University. She is passionate about diversity and inclusion in STEM; she has conducted research on diversity in named patent inventors, working with companies to measure and address diversity gaps, and she is an active board member at a STEM education non-profit.
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