The AI wars between tech giants and closed models vs open-source models are underway, with OpenAI's GPT line facing competition from Meta's Llama 3.1, a strong open-source model. The latest Llama model, Llama 3.1, offers comparable performance to proprietary models while providing the freedom to study, modify, and deploy without restrictions. RAG (Retrieval-Augmented Generation) is an AI technique that combines large language models with external knowledge retrieval. It involves a retrieval step searching a knowledge base for relevant information, which is then fed into the LLM along with the original query. A vector database, like Neon, enables efficient similarity searches of embeddings, allowing for more accurate and up-to-date responses. The tech stack used in this project includes Llama 3.1, Neon, and OctoAI, which simplify deployment and management of open-source AI models. By creating a vector database in Neon, users can efficiently store and query their data, making it ideal for storing and retrieving large amounts of text or documents. The RAG model with Llama 3.1 combines user input with retrieved quotes from the vector database to generate more relevant responses. This approach enables the creation of highly relevant AI applications without the high costs associated with proprietary models.