Building RAG-based LLM Applications

Today’s Large Language Models(LLMs) possess immense Natural Language capabilities, but fine-tuning models is a herculean task. Retrieval-augmented generation (RAG) has come to the rescue as it addresses the need to use custom data sets without fine-tuning the model extensively. In this episode of the AI Monetisation Podcast, our host, co-founder, and CEO Binny Mathews interviews industry expert Ambujesh Upadhyay, Lead Data Scientist at Harman, with over 8 years of experience in companies like Samsung and Intel. They discuss various RAG use cases in the industry and the benefits and challenges of using RAG. With LLM becoming more prevalent, data governance has become an essential issue that Amjuesh focuses on and discusses the potential for regular developers to integrate Natural Language Processing(NLP) into their applications, emphasizing the need for understanding the basics in this rapidly evolving domain within Generative AI. Ambujesh also makes recommendations for people transitioning into NLP and AI roles and ways to keep pace with the evolving landscape of AI and NLP, thus providing valuable insights for both beginners and experienced professionals. For more details on this episode, visit our dedicated podcast page - ⁠https://bit.ly/4jgOcqa⁠ProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production-⁠https://bit.ly/4jgNZmS

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