It is necessary to include data warehouses in contemporary data processing frameworks to provide comprehensive support for efficient decision-making procedures. This study aims to evaluate the exploitation of data warehouses as a knowledge base inside a Retrieval-Augmented Generation (RAG) model. This model combines retrieval mechanisms with generative models to improve information retrieval and response generation in artificial intelligence systems. Several necessary procedures are the subject of this research. These procedures include the preparation of data via the use of Databricks, the production of online tables, and the transformation of these tables into embeddings. Databricks offers a robust data engineering platform, enabling practical data input, cleaning, and structuring into Delta tables. This is followed by building online tables, making it easier to get data quickly. They are then converted into embeddings, which can capture the semantic substance of the data. These online tables are subsequently altered. The embeddings are kept in a repository, and the RAG model makes use of them to create replies consistent with the context in which they are being used. RAG models can efficiently harness enormous data repositories, as shown by the findings of this research, which reveal considerable increases in the speed at which data is retrieved and the precision of responses. By implementing best practices and using Databricks' capabilities, businesses can improve their AI-driven decision-making processes. This approach is advantageous for various purposes, including customer assistance, data analysis, and strategic planning. By doing more study in the future, it will be possible to investigate the applicability of this technique across a variety of domains and the incorporation of sophisticated generative models to enhance performance.