Fivetran enables organizations to access large volumes of data necessary to train AI models by providing a simple workflow for integrating data from various sources into a central repository. This allows for the combination and analysis of schemas from disparate sources, making it easier to build generative AI models such as retrieval-augmented generation (RAG). RAG consists of supplementing an existing generative AI model with additional facts and context to produce more factual and relevant outputs. Fivetran supports integration of large bodies of text within structured data from various sources, including GitHub, Salesforce, and Zendesk, making it a practical option for organizations looking to leverage the power of off-the-shelf generative AI. By utilizing Fivetran's governed data lake or repository, organizations can add modularity to their architecture, allowing them to easily change vector embeddings and chunking strategies without resynchronizing all data from sources. The process also involves embedding raw data as numerical representations called vectors in a vector database, which can be attached to user prompts and sent to a foundation model for more accurate and relevant answers.