What is RAG?

RAG, or Retrieval-Augmented Generation, is a technique that enhances the output of large language models (LLMs) by incorporating information from external knowledge sources, enabling more accurate and contextually relevant responses.

RAG allows LLMs to access up-to-date and domain-specific information without retraining, improving factual accuracy and reducing hallucinations by grounding responses in verifiable knowledge sources.

RAG uses a knowledge library that stores information from various sources in a common format, processed into numerical representations (embeddings). When a query is made, the system searches this library to retrieve the correct contextual information, which is then used by the LLM to generate responses.

A customer service chatbot powered by RAG can retrieve specific company policies, product manuals, or recent updates to provide accurate and current answers that go beyond its base training data.

Connected to Vector Databases, Semantic Search, Embeddings, Language Models, and Knowledge Management systems.