Retrieval-Augmented Generation (RAG) has become one of the most powerful patterns for deploying large language models (LLMs) in real-world environments. Instead of relying solely on pre-trained knowledge, RAG architectures connect models to external data sources such as databases, APIs, document repositories, and knowledge bases. This significantly improves factual accuracy, reduces...

