Description

The SLAIC Session 2 Part I, held on May 9th, was conducted by Ms. Ishani Udeshika, a Data Scientist at Zone24x7. She delivered an insightful introduction to Large Language Models, breaking down their capabilities as well as the limitations they face in real-world use cases, especially when operating without current or domain-specific knowledge. To address this challenge, she introduced the concept of Retrieval-Augmented Generation (RAG), a powerful technique that enhances LLM performance by combining them with external, retrievable knowledge. Her session offered a balanced mix of theoretical background and practical insight, including a brief demonstration of how RAG functions in real scenarios. Participants were highly engaged, with an interactive Q&A segment allowing them to ask questions and gain a deeper understanding of the topic.