RAG (Retrieval-Augmented Generation)
A technique that injects fresh, external knowledge into LLM prompts at run-time.
Detailed Definition
Retrieval-Augmented Generation (RAG) augments an LLM with a retrieval step: before the model answers, a retriever searches structured or unstructured data sources (vector DBs, documents, SQL, APIs) for relevant snippets. These snippets are appended to the prompt, grounding the model’s response in up-to-date, domain-specific facts and sharply reducing hallucinations. RAG is now a foundational pattern for enterprise chatbots, knowledge assistants, and analytic agents.
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