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RAG (Retrieval-Augmented Generation)

Giving AI access to your own data

What is RAG (Retrieval-Augmented Generation)?

A technique where an AI model retrieves relevant information from a knowledge base before generating a response. Instead of relying only on training data, RAG lets you ground AI answers in your actual documents, policies, or content. Essential for accurate, non-hallucinating AI applications.

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LV’s AI hub

Original context on AI applied to creation, research, and systems.

Creator OS

Useful when AI is part of a content workflow that still needs human judgment.

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In plain words

"Think of it like an open-book exam — the model looks up your docs before answering."

How it works

Key takeaways

  • Grounds responses in your real documents

  • Reduces hallucination significantly

  • No retraining needed — just update the knowledge base

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Real-world example

A customer asks your chatbot 'What's your refund policy?' RAG retrieves your actual policy document, feeds it to the LLM, and the answer cites real clauses instead of hallucinating.

Related terms

LLM (Large Language Model)

AI models that understand and generate text

Prompt Engineering

Crafting inputs to get the best AI outputs

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