Without RAG, the LLM takes the user input and creates a response based on information it was trained on—or what it already knows. With RAG, an information retrieval component is introduced that utilizes the user input to first pull information from a new data source. The user query and the relevant information are both given to the LLM.

Dec 5, 2023 · How does RAG work? A typical RAG process, as pictured below, has an LLM, a collection of enterprise documents, and supporting infrastructure to improve information retrieval and answer construction.

Jul 2, 2023 · And how does it work? If you’ve ever pondered these questions, you’re in the right place. This article is designed to demystify RAG, breaking it down into simple, easy-to-understand terms.

4 days ago · How Does RAG Work? RAG operates in two main steps, called retrieval and generation, making it both efficient and smart. Retrieval Step: When you pose a query—e.g., “What’s the current on Mars missions in 2025?”—the system does not merely search its memory. It employs a retriever, usually fueled by dense vector search (sophisticated ...

Feb 10, 2025 · RAG addresses this limitation by incorporating a retrieval mechanism that allows the model to access external databases or documents in real-time. ... How does Retrieval-Augmented Generation work? The system first searches external sources for relevant information based on the user’s query Instead of relying only on existing training data. 1 ...

Oct 30, 2024 · How does RAG work? RAG involves two phases: ingestion and retrieval. To understand these concepts, it helps to imagine a large library with millions of books. The initial “ingestion” phase is akin to stocking the shelves and creating an index of their contents, which allows a librarian to quickly locate any book in the library’s collection.

Mar 14, 2025 · How Does RAG Work? Now that you understand what RAG is, let’s look at the steps involved in setting up this framework: Step 1: Data collection. You must first gather all the data that is needed for your application. In the case of a customer support chatbot for an electronics company, this can include user manuals, a product database, and a ...

Mar 20, 2025 · How does RAG work? The key difference between regular LLMs and RAG lies in how RAG combines text generation with real-time information retrieval. Instead of relying solely on training data, RAG actively fetches information from trusted sources—whether that's your company's documentation, industry reports, or verified databases. ...

Mar 28, 2025 · How Does Retrieval-Augmented Generation Work? Without Retrieval-Augmented Generation, an LLM generates responses based only on its training data. RAG adds a retrieval step that pulls relevant information from external sources using the user’s query. This external data, combined with the original query, is then passed to the LLM. As a result ...

Here’s a simplified overview of the RAG pipeline: Indexing: Your content (e.g. docs, wikis, product information) is split into smaller chunks and converted into vectors using an embedding model. These vectors are stored in a vector database. Retrieval: When a user asks a question, it’s also embedded into a vector and used to find the most relevant chunks from the vector database.