From the course: Level up LLM applications development with LangChain and OpenAI

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Breaking down the RAG pipeline

Breaking down the RAG pipeline

From the course: Level up LLM applications development with LangChain and OpenAI

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Breaking down the RAG pipeline

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- The RAG pipeline consists of two main components. First, the information retrieval from an external data source. Then you have the content generation that works by adding context to the content generated by the language model in order to enhance the answer based on the information retrieval and the user query. And this is what we call augmented content generation. So the RAG process helps users to get the contextually-rich and accurate responses that they're looking for. And so the benefits of RAG are multiple. It's going to allow to receive up-to-date and current information by retrieving context from an external data source, and allow the language models to provide current and relevant answers. That's going to allow to improve the accuracy and enhance the relevance, meaning that the retrieval process answers that the generated text is closely aligned with the given search. So how does it work? The actual RAG chain starts with a user query, a question. Then it's going to trigger…

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