Retrieval

Accessing and incorporating relevant information from external knowledge sources

Overview

Retrieval is the process of accessing and incorporating relevant information from external knowledge sources to inform AI model responses. This capability is fundamental to Retrieval-Augmented Generation (RAG) systems, where models combine their trained knowledge with real-time information access. By retrieving contextually relevant information, systems can provide more accurate, up-to-date, and factual responses while reducing hallucinations.

Benefits

  • Improved accuracy in AI responses
  • Reduced hallucinations and false information
  • Access to up-to-date information
  • Enhanced contextual understanding
  • Better factual grounding

Implementation

Retrieval works by searching and accessing information from databases, documents, or other external sources based on the current context and query requirements. The process typically involves:

  • Indexing and storing information in accessible formats
  • Matching queries with relevant content
  • Incorporating retrieved information into model responses

Key Applications

  • Fact verification and validation
  • Knowledge augmentation for AI systems
  • Enhanced response generation
  • Real-time information access
  • Document and data retrieval