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RAG (Retrieval Augmented Generation)

RAG (Retrieval Augmented Generation) is a technique that combines language model text generation with information retrieval from an external knowledge base. Instead of relying solely on the model's internal knowledge (which has a cutoff date and can hallucinate), the RAG system first searches for relevant information in a document database, then provides it to the model as context in the prompt, and finally asks the model to generate the response based on these verified sources.

Definition

RAG is especially valuable in business contexts where information changes frequently (regulations, prices, procedures), where factual accuracy is critical (legal, compliance, medicine), or where you want to limit the model's answers to a specific corpus of company documents.

Understanding how RAG works matters for professionals who want to integrate AI into business workflows robustly and reliably, an area Grace covers in its advanced scenarios.

Related terms

• Embedding
• LLM (Large Language Model)
• AI Hallucination
• Context Window
• AI Workflow

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• Embedding
• API

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