AI Glossary
RAG (Retrieval-Augmented Generation)
Understanding AI Terminology
A technique that enhances AI responses by retrieving relevant information from external sources.
What It Means
Retrieval-Augmented Generation (RAG) combines language models with information retrieval systems. Before generating a response, the system searches a knowledge base for relevant documents, then includes that information in the prompt. This reduces hallucinations, enables access to current information, and allows AI to work with proprietary data without fine-tuning.
Examples
- Searching company docs before answering policy questions
- Querying recent news before discussing current events
- Looking up code documentation for technical answers
How This Applies to ARKA-AI
ARKA-AI's research tools use RAG-like approaches to provide more accurate, grounded responses with citations.
Frequently Asked Questions
Common questions about RAG (Retrieval-Augmented Generation)
RAG is easier to update (just change the documents), provides transparent sources, and doesn't require model retraining. It's ideal for information that changes frequently.
RAG significantly reduces hallucinations by grounding responses in retrieved documents, but doesn't eliminate them entirely. The model might still misinterpret or incorrectly summarize sources.
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