Leveraging Large Language Models for Customer Service and Support Transformation
Driven by advancements in large language models (LLMs) such as OpenAI’s ChatGPT and Google’s Bard, the customer service and support landscape is undergoing a significant transformation.
This talk proposal focuses on harnessing the power of LLMs to augment human agents and revolutionize support interactions. It digs into the advantages of LLMs, such as their quick deployment and scalability, and shows how they improve customer service speed, efficiency, and consistency.
Additionally, the talk emphasizes the criticality of enterprise-specific content for generating credible responses, presenting a three-part approach that combines system instructions, user questions, and relevant documentation. The proposal also addresses the challenge of centralizing and managing unstructured data assets, providing insights into leveraging LLMs for support-driven decision-making.
Attendees will gain valuable insights into the potential of LLMs in customer service and support, understand the importance of contextualization, and learn effective strategies for content management to ensure accurate and personalized responses.
Data Science Manager at Arthur J Gallagher
Lovedeep Saini, Ph.D., has over 15+ years of experience in data science and analytics. She is passionate about machine learning and AI-driven innovations that help businesses to grow and improve their outcomes.
She leads leveraging large datasets to develop predictive models and other insights to solve challenging, high profile business problems.
Lovedeep has been with Gallagher since July 2018 and holds a PhD in experimental high energy physics.
Prior to joining Gallagher, she was a particle physicist in academia, where she co-discovered Higgs boson and measured properties of several other exotic physics phenomena like extra dimensions and dark-matter; using statistical and machine learning techniques.
Having been trained at Large Hadron Collider, arguably the biggest scientific experiment of human history – she brings to the table a taste for problem solving, natural curiosity and excellent background in computer programming, statistics, probability and research management.
Outside work, she is a marathon runner and enjoys spending time reading and hiking with audiobooks and podcasts.