What Is RAG (Retrieval Augmented Generation)?
Retrieval Augmented Generation (RAG) is an AI architecture that enhances language model responses by first retrieving relevant information from external knowledge sources, then using that context to generate more accurate and grounded answers.
How RAG (Retrieval Augmented Generation) Works
RAG solves a fundamental limitation of large language models: they can only rely on what they learned during training. A RAG system first searches a knowledge base — such as company documents, databases, or the web — for information relevant to the user's query. It then feeds the retrieved context into the language model along with the original question, enabling the model to produce answers grounded in real, current data. This dramatically reduces hallucinations and allows AI to work with proprietary or frequently updated information without retraining the model.
Real-World Examples
An enterprise chatbot that searches internal company wikis before answering employee questions about HR policies
Perplexity AI retrieving and citing live web sources when answering user queries
A legal AI assistant pulling relevant case law from a database before drafting a legal brief
RAG (Retrieval Augmented Generation) on Vincony
Vincony offers RAG chatbot capabilities, letting users connect their own documents and knowledge bases to AI models for grounded, accurate responses.
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