In the insurance industry, there are vast amounts of data stored in documents like policies, claim details, and FAQs. Providing answers to customers' queries quickly and accurately is crucial for satisfaction and efficiency. The objective of this project is to develop an AI-powered Q&A assistant using Retrieval Augmented Generation (RAG) and Snowflake Cortex Search.
RAG (Retrieval-Augmented Generation) integrates large language models with external information retrieval. Upon the user asking a question, the system brings back candidate documents from a knowledge base. The documents act as context to the LLM to generate a proper and informative response.
This project demonstrates how to build a robust and effective insurance Q&A assistant by combining the strengths of Retrieval Augmented Generation (RAG) with Snowflake Cortex Search. Using Snowflake's semantic search capability, we can quickly retrieve contextually relevant information from an insurance document knowledge base. The retrieved context is then used as input to a Large Language Model (LLM) to generate accurate and informative answers to user queries.