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RAG (Retrieval Augmented Generation)
AI first searches for relevant documents before answering.
Explanation
RAG combines information search with text generation. Instead of just answering from the training, the system first searches a knowledge base and uses the documents found as a basis.
How it works
1) User asks a question → 2) System searches relevant documents in a vector database → 3) Texts found are passed to the LLM as context → 4) LLM generates an answer based on the documents.
Example
An internal AI assistant that answers company policy questions by finding and citing relevant policy documents.
Why it matters
RAG is the standard approach to equip LLMs with company-specific knowledge - without expensive follow-up training.