AI Glossary
What is RAG?
Retrieval-Augmented Generation — a technique that enhances AI responses by retrieving relevant documents and feeding them as context.
RAG explained
RAG is one of the most impactful techniques in enterprise AI. Instead of relying solely on the LLM's training data, a RAG system first searches a knowledge base (your documents, database, or the web) for relevant content, then provides that content to the LLM as context. This allows the AI to give accurate, up-to-date answers based on your specific data — dramatically reducing hallucinations.
Frequently asked questions
What is RAG in AI?
RAG stands for Retrieval-Augmented Generation. It's a technique where an AI system retrieves relevant documents from a database before generating a response, making answers more accurate and grounded.
Why is RAG important?
RAG reduces hallucinations by giving the AI access to real, up-to-date information from your knowledge base instead of relying solely on training data.
Do I need to know RAG to use AI skills?
Not necessarily. Most AI skills on AISkillsKart work directly with standard ChatGPT, Claude, or Gemini interfaces without any RAG setup.
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