Vector databases are a type of database that stores numeric representations or vectors of data, allowing advanced machine learning algorithms to make sense of unstructured data and return relevant results. In the insurance industry, vector databases can speed up and increase the accuracy of claim adjustment by enabling adjusters to quickly compare images and retrieve complementary information stored in the same database. Vector Search is a powerful tool that unlocks access to unstructured data, and when combined with Retrieval Augmented Output (RAG), it enables LLMs to generate more reliable and accurate outputs for tasks such as natural language processing, computer vision, and content generation.