This hands-on workshop will focus on the practical applications of Retrieval-Augmented Generation (RAG) AI models and LLMs in underwriting. We'll also touch on some use cases around multi-modal models. It aims to provide actuaries with a solid understanding of how these technologies can be applied, emphasizing a Python-based implementation.
Learning Objectives:
Understand the fundamentals of RAG AI models and their relevance in the context of actuarial science.
Explore real-world use cases of RAG AI in risk assessment, predictive modeling, and data analysis.
Develop basic skills in implementing Python-based RAG AI models for actuarial tasks.
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