Retrieval Augmented Generation (RAG)

Overview

RAG (Retrieval Augmented Generation) combines information retrieval with generative models to enhance the accuracy and contextual relevance of generated text. In RAG, a retrieval component first retrieves relevant documents or passages from a knowledge source (such as a vector database, knowledge base, or web search), and then a generative model uses this retrieved information to produce the final output. This allows the model to leverage external knowledge and generate more informed and factually grounded responses, mitigating some of the limitations of models trained solely on data internal to the model.

Combining Generation and Retrieval

RAG combines AI text generation with information retrieval from external sources to produce more accurate and contextual responses.

How it Works

The system first retrieves relevant data from external sources and then generates responses based on both the retrieved information and its internal knowledge.

Enhanced Accuracy

This approach significantly improves the accuracy, context, and grounding of generated text by incorporating real-world information from trusted sources.

Related Concepts

• Retrieval
• Grounding
• Knowledge Base
• Vector Database
• Information Retrieval
• Semantic Search
• Text Generation