Retrieval-augmented generation (RAG) is an important technique for improving the outputs of large language models (LLMs) by preventing issues like hallucinations and extending their use cases. To effectively implement RAG, it is essential to follow best practices such as consistently evaluating model outputs to identify and address potential issues through human oversight and various tests like consistency, load, and edge-case testing. Providing context by linking specific sources and explaining how outputs are generated can enhance user trust in LLMs. Utilizing product integration data from customer systems can improve the accuracy and diversity of data fed into LLMs, ultimately resulting in better outputs. Merge, a leading unified API solution, facilitates these processes by offering a suite of Integration Observability features and normalized data, allowing efficient data collection and maintenance of integrations to ensure minimal downtime and high-quality LLM outputs.