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RAG Architecture Deep Dive | LinkedIn

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Retrieval Augmented Generation (RAG) is a technique for augmenting Large Language Model (LLM) knowledge with additional data. In a standard Gen-AI application using LLM as its sole knowledge source, the model generates responses solely based on the input from the user query and the knowledge it has been trained on. It does not actively retrieve additional information beyond what is encoded in its parameters during fine-tuning or training. In contrast, an RAG architecture integrates both retrieval-based and generative components. It includes a retriever component that obtains relevant information from a large collection of text (typically referred to as a corpus). This corpus is stored in an embedding database, most commonly referred to as a vector database. The retrieved information is then used by the generative component to produce responses. The LLM is at the heart of the generative component. Typically, most conversations about RAG architecture focus on the retrieval process and th

Retrieval Augmented Generation (RAG) is a technique for augmenting Large Language Model (LLM) knowledge with additional data. In a standard Gen-AI application using LLM as its sole knowledge source, the model generates responses solely based on the input from the user query and the knowledge it has been trained on. It does not actively retrieve additional information beyond what is encoded in its parameters during fine-tuning or training. In contrast, an RAG architecture integrates both retrieval-based and generative components. It includes a retriever component that obtains relevant informati

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