The article explores the potential of Retrieval Augmented Generation (RAG) techniques to enhance the quality of responses from language models, such as ChatGPT, Gemini, and Claude. It illustrates how RAG combines the retrieval of relevant information with text generation, enabling models to provide more accurate and up-to-date responses, while reducing hallucinations. The article examines the different stages of the RAG process, exploring best practices for query classification, information retrieval, result reprocessing, document summarization, and refinement of the generating model. The article also describes the application of RAG in business and multimodal contexts, showing how it can improve customer service, optimize internal processes, and extend the capabilities of language models to different types of data.