Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to deliver precise, up-to-date responses without retraining the model. RAG architecture comprises a knowledge base, retriever, and generator, where the knowledge base stores indexed information, the retriever finds relevant document fragments, and the generator creates responses by merging these with user queries. Despite its advantages, RAG systems face challenges in data ingestion, retrieval accuracy, and performance, necessitating strategies like hierarchical and semantic chunking, query decomposition, and reranking. Production-grade RAG systems must optimize latency, manage operational costs, and ensure security, employing techniques like caching, batching, and distributed architecture. Continuous evaluation is crucial for maintaining quality, combining automated metrics with human review, while scalability solutions involve cost and performance optimization and robust security measures. The success of RAG systems relies on thoughtful design, balancing efficiency and accuracy, with managed RAG-as-a-service platforms like Ragie offering streamlined alternatives for development teams.