Retrieval-Augmented Generation- RAG

Definition

Retrieval-Augmented Generation (RAG) is an artificial intelligence approach that equips a large language model (LLM) with the ability to access external knowledge sources in order to generate answers that are more up-to-date, precise, and verifiable. It combines information retrieval and text generation: first, relevant information is retrieved from databases or document collections, and then this content is incorporated into the generated response.

A RAG-based system integrates external knowledge into generation by retrieving documents from an index that match the query (“retrieval”) and providing them to the LLM as context for the answer. The goal is to go beyond the static knowledge stored during training and include both current and domain-specific content.

Target Groups

  • Companies with chatbots, search systems, or internal knowledge management solutions
  • Research institutions requiring access to up-to-date publications
  • Industries with high accuracy demands (e.g., medicine, law, technology)
  • Developers of AI systems with domain-specific expertise

Benefits

  • Up-to-dateness: Access to continuously refreshed data sources
  • Accuracy: Reduction of hallucinations through verified information
  • Traceability: Source references enable verification
  • Domain specificity: Utilization of internal or industry-specific data
  • Efficiency: No need for full retraining of the model

Key Components

  • Data index: Structured storage of searchable documents
  • Retriever: Algorithm (e.g., Dense Passage Retrieval, BM25) to identify relevant passages
  • Augmentation: Embedding the retrieved content into the prompt
  • Generator: LLM that produces the response based on the enriched context

Priorities

  • High relevance of retrieved results
  • Scalability for very large knowledge bases
  • Security & data protection, including compliance requirements
  • Transparency through disclosure of used sources

Trends

  • Combination with multimodal data (text, image, audio, video)
  • Integration into enterprise search and assistant systems
  • Use in real-time applications with live data connectivity