The text discusses the use of Retrieval Augmented Generation (RAG) in AI systems, particularly in domain-specific generative models. RAG involves dynamically retrieving relevant context from external sources and integrating it with user queries to generate responses. The system uses a vector database, LLM, embedding model, and orchestration tool to build a basic RAG system. To evaluate the performance of RAG systems, the text introduces Galileo's RAG analytics, which provide detailed metrics for optimization and evaluation. These metrics include Chunk Attribution, Chunk Utilization, Completeness, and Context Adherence. The text also discusses how to build a standard QA using the RAG chain, utilizing GPT-3.5-turbo as the LLM and the same vector DB for retrieval. It then presents experiments to improve the performance of the RAG system, including adjusting the encoder, chunking strategy, top k value, and LLM model. The results show significant improvements in adherence, cost reduction, and latency decrease while maintaining a balance between performance and cost.