Retrieval augmented generation (RAG) is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases.
Retrieval-augmented generation (RAG) helps businesses use generative AI by connecting LLMs to internal data.
This is where retrieval augmented generation (RAG) comes in. Broadly speaking, RAG is a method for giving AI models access to additional external information that they haven't been trained on. Crucially, it allows AI models to access new and up-to-date information without needing to be retrai...
Get to know and directly engage with senior McKinsey experts on RAG. Lareina Yeeis a senior partner in McKinsey’s Bay Area office, whereMichael Chuiis a senior fellow andRoger Robertsis a partner;Mara Pomettiis a consultant in the London office;Patrick Wollneris a consultant in the Vienna ...
Companies are using RAG to turn their knowledge bases from static repositories into dynamic resources that adapt to user needs. This is particularly powerful for large organizations, where finding the right information quickly can significantly impact productivity. ...
Here’s the basic workflow of how a Generative AI solutions with Retrieval Augmented Generation (RAG) works: Query Input: When a user submits a query, it is processed by the retriever. Information Retrieval: The retriever searches through external data sources to find relevant information. This ...
If you’re wondering whether RAG can exist without artificial intelligence, the short answer is no. The “generation” capability of RAG relies on AI systems, which means you need a generative model to produce answers to your questions or prompts. Let’s review how this process works. ...
Interaction with Retriever: The generator doesn’t work in isolation; it uses the context provided by the retriever to inform its response, ensuring that the output is not just plausible but also rich in detail and accuracy. The Workflow of a Retrieval-Augmented Generation (RAG) System Image So...
The workflow in the first section of this page represents the architecture of a Simple RAG system. This type of system is best suited for use cases such as customer support bots, FAQ systems, or any use case that necessitates accurate responses from a limited scope of information, such as ...
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