Retrieval Augmented Generation (RAG)
Definition
Why "Retrieval Augmented Generation (RAG)" Matters in AI
Understanding retrieval augmented generation (rag) is essential for anyone working with artificial intelligence tools and technologies. As a core concept in Large Language Models, retrieval augmented generation (rag) directly impacts how AI systems like ChatGPT, Claude, and Gemini process and generate text. Whether you're a developer, business leader, or AI enthusiast, grasping this concept will help you make better decisions when selecting and using AI tools.
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Frequently Asked Questions
What is Retrieval Augmented Generation (RAG)?
Connects a model to your knowledge base so it can cite and ground answers in your content—improving accuracy and reducing hallucinations. Example: Search Confluence → retrieve pages → generate a cited...
Why is Retrieval Augmented Generation (RAG) important in AI?
Retrieval Augmented Generation (RAG) is a intermediate concept in the llm domain. Understanding it helps practitioners and users work more effectively with AI systems, make informed tool choices, and stay current with industry developments.
How can I learn more about Retrieval Augmented Generation (RAG)?
Start with our AI Fundamentals course, explore related terms in our glossary, and stay updated with the latest developments in our AI News section.