What is RAG (Retrieval-Augmented Generation)?
Combining AI with real-time information retrieval from external knowledge bases.
Definition
Retrieval-Augmented Generation (RAG) combines the power of LLMs with external knowledge bases. Instead of relying solely on training data, RAG systems first search relevant documents, then use that information to generate more accurate, up-to-date responses. Perplexity uses RAG to provide cited answers.
Examples
Why It Matters
RAG reduces hallucinations and enables AI to access current information beyond its training cutoff.
Related Terms
Large Language Model (LLM)
An AI system trained on vast text data to understand and generate human-like text.
AI Hallucination
When an AI generates false or fabricated information that sounds plausible.
Grounding (AI)
Connecting AI responses to verifiable facts and real-world data sources.
Embeddings
Numerical representations of text that capture semantic meaning for AI processing.
Common Questions
What does RAG (Retrieval-Augmented Generation) mean in simple terms?
Combining AI with real-time information retrieval from external knowledge bases.
Why is RAG (Retrieval-Augmented Generation) important for AI users?
RAG reduces hallucinations and enables AI to access current information beyond its training cutoff.
How does RAG (Retrieval-Augmented Generation) relate to AI chatbots like ChatGPT?
RAG (Retrieval-Augmented Generation) is a fundamental concept in how AI assistants like ChatGPT, Claude, and Gemini work. For example: Perplexity searching the web before answering Understanding this helps you use AI tools more effectively.
Related Use Cases
AI Models Using This Concept
See RAG (Retrieval-Augmented Generation) in Action
Council lets you compare responses from multiple AI models side-by-side. Experience different approaches to the same prompt instantly.