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A RAG pipeline is the process of retrieving information and producing an AI-generated answer. Even though there could be variations, the process usually starts with the collection and preparation of documents or other sources of knowledge. These sources of information are then split into smaller parts and turned into a searchable format which is stored in an appropriate retrieval system. As a user enters a question, the system looks for related information pieces and fetches those which are most relevant to the task. Afterward, this information is delivered to the language model as context. The language model then produces an answer based on that information. That is the concept of the retrieval-augmented generation. A good RAG pipeline can help AI applications operate with domain-specific information.