Core Viewpoint - The article discusses the concept of Retrieval-Augmented Generation (RAG) and its significance in enhancing the accuracy and relevance of generative AI models by allowing them to access external data, thereby reducing instances of "hallucination" [1][6]. Group 1: RAG and Vectorization - RAG solutions enable generative AI models to retrieve data they were not originally trained on, improving the contextual accuracy of their responses [1]. - One of the best methods to implement RAG is through vectorization, which converts text, images, or other information into a numerical format for easier processing by computers [3][5]. - Semantic search, which relies on vectorization rather than keyword indexing, allows for more precise information retrieval by capturing underlying meanings [4][5]. Group 2: VaaS Implementation - FactSet has developed a platform called "Vectorization as a Service" (VaaS) that simplifies the process of storing and retrieving data for AI solutions, allowing employees to upload documents or connect to databases for quick vectorization [7][11]. - VaaS enables the creation of centralized knowledge bases, making it easier for teams to access and search through various company information sources [12]. - Since the launch of VaaS, employees have created hundreds of specialized knowledge bases, enhancing information discoverability and usage [12]. Group 3: Impact of VaaS - VaaS has automated the data preparation process for AI solutions, significantly increasing the number of tokens processed by the system since its launch in June 2024 [13][17]. - The centralized management of data through VaaS facilitates easier access and collaboration among employees while maintaining data flexibility [17]. - The rapid development of AI solutions makes it increasingly important for companies to invest time in developing robust DevOps solutions, which VaaS supports by empowering employees of all skill levels [20].