Retrieval-Augmented Generation (RAG) is a prominent pattern in modern large language model (LLM) development, combining information retrieval from data sources with response generation using LLMs. This approach, which includes various architectures such as keyword-based RAG and those using embeddings and Vector DBs, is applied in scenarios like internet searches, knowledge querying, and dataset summarization. While RAG facilitates the creation of MVPs for generative AI applications, developers face challenges in improving quality and reducing hallucinations. Best practices for testing RAG systems involve creating test data, conducting AI evaluations with models like GPT-4, and refining the evaluation process to distinguish between retrieval and generation system failures. Enhancements to the testing framework include evaluating specific stages of the RAG pipeline and breaking down fact evaluation into compliance and completeness assessments. These methodologies aim to systematically improve RAG system performance by identifying and addressing specific failure points.