This session explores the practical aspects of Generative AI and Large Language Model (LLM) implementation. Our discussion will encompass common use cases and best practices for employing LLMs to achieve optimal outcomes in various scenarios. Depending on the requirement, LLMs can be useful right away or may require additional customization. At times, effort must be devoted to creating prompts, and occasionally, approaches like Retrieval Augmented Generation (RAG) may be necessary to link personalized data to the LLM's capabilities. Additionally, fine-tuning the LLM may be required to achieve desired results when its existing capabilities are insufficient. During our session, we will explore some of the most common and popular methods and understand when to apply them in different circumstances.

Jyotika Singh is an accomplished Data Science leader and practitioner, NLP book author, speaker, researcher, and mentor. She currently works as a Principal Applied Scientist at Oracle building ML and Generative AI solutions. In the past, Jyotika was the Director of Data Science at Placemakr and also headed the Data Science team at ICX Media where she developed novel solutions that formed the foundation of the business, culminating in multi-million dollar deals and a successful acquisition. Jyotika's impact in AI extends to her prolific patent portfolio, global conference speaking engagements, and commitment to open-source software. She has also received various awards, including the Leadership Excellence in Technology award in 2021, DataIQ Top 100 Most Influential People in Data 2022, and others. Jyotika is dedicated to promoting women in STEM through mentorship efforts and has been recognized as one of the Top 50 Women of Impact in 2023 and 2024 by Women Impact Tech.