Nithish Raghunandanan is an engineer who loves to build products that solve real world problems in short spans of time. He has experienced different areas of the industry having worked in diverse companies in Germany and India. Apart from work, he likes to travel and interact and engage with the tech community through Meetups & Hackathons. In his free time, he likes to try stuff out by hacking things together.

Presentations

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Evaluating the Effectiveness of Retrieval Augmented Generation (RAG) in Real-World Applications

With the rise of large language models (LLMs) enhanced by retrieval augmented generation (RAG), it has become essential to develop rigorous evaluation methodologies to assess their effectiveness across diverse use cases. RAG combines a model's generative capabilities with information retrieval, allowing for contextually relevant responses grounded in up-to-date, factual knowledge. This talk will focus on the unique challenges and best practices for evaluating RAG applications covering quantitative metrics (e.g., accuracy, relevance, etc).

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