Retrieval-augmented generation (RAG) combines large language models with external knowledge retrieval to produce accurate responses. RAG systems can improve accuracy and relevance in various applications, such as healthcare, e-commerce, and customer support. Implementing best practices, including optimizing embedding models, retrievers, and language models, is crucial for enhancing performance. Continuous monitoring and evaluation are essential to ensure the system remains effective and adapts to evolving data needs. Common pitfalls, such as inadequate chunking, poor prompt design, and overlooking key metrics, can undermine optimization. By addressing these challenges and implementing strategies like consistent feedback loops, controlled A/B testing, and accurate data interpretation, organizations can reduce error rates and improve system performance.