Was ist Retrieval Augmented Generation (RAG)

Definition

Retrieval Augmented Generation (RAG) is an AI architecture that combines generative language models with an external knowledge base to generate more precise, up-to-date, and context-specific answers, leveraging a company's own proprietary data.

Explanation

In the B2B and SaaS sectors, standard AI models often fall short because they lack access to internal company data or real-time information. RAG addresses this challenge by, upon a user query, first retrieving relevant documents (e.g., product catalogs, CRM data, or service documentation) from a vector database (the 'Retrieval' step). These retrieved documents are then provided as context to the language model. Only then does the AI generate the final response (the 'Generation' phase), ensuring it's based on verified facts rather than solely on its pre-trained knowledge. In a sales context, this translates to AI assistants being able to answer customer inquiries with highly personalized, precise responses, and without the risk of hallucinations. Consequently, RAG emerges as a pivotal technology for streamlined knowledge management systems and automated customer interactions.