Retrieval-Augmented Generation (RAG) is an emerging AI approach that is gaining traction across industries. In this episode, hosts Mischa Getter, Heather Harris, PhD, and Afnan Waseem introduce RAG, explaining what it is and why organizations are increasingly adopting it. They provide a high-level definition of RAG and explore how it differs from traditional AI models, making it a valuable tool for improving response accuracy and reducing misinformation.
The discussion delves into the mechanics of RAG, offering a straightforward explanation of how it retrieves external knowledge to enhance AI-generated responses. The hosts highlight real-world applications, showcasing how Big Tech and other industries are leveraging RAG to improve information quality. They also examine key benefits, such as enhanced response reliability and the ability to mitigate inaccuracies in AI-generated content.
While RAG presents significant advantages, it also comes with challenges. The episode addresses concerns like knowledge gaps and hallucinations, explaining the risks and limitations that organizations must consider. The hosts explore how companies are navigating these challenges and why RAG remains a compelling choice for AI development. Tune in to gain a deeper understanding of this transformative technology and its impact on the future of AI.
Misha Getter: https://www.linkedin.com/in/michael-getter/
Heather Harris, PhD: https://www.linkedin.com/in/heatherdawnharris/
Mohammed Afnan Waseem: https://www.linkedin.com/in/mohammed-afnan-waseem/
TPAC on LinkedIn: https://www.linkedin.com/company/asae-tpac/
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