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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.

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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