Definition
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model outputs by first retrieving relevant documents from external sources, then using that context to generate more accurate, grounded responses.
In Depth
RAG addresses the fundamental limitation of LLMs: their training data has a cutoff date and they can hallucinate facts. In a RAG pipeline, a retrieval step fetches relevant documents, web results, or database records before the LLM generates a response. This grounds the output in real data. For applications needing current information, pairing RAG with a real-time search API like Scavio ensures the retrieval step always returns fresh results. Common RAG architectures use vector databases for stored documents and search APIs for live web data, combining both for comprehensive context windows.
Example Usage
A customer support bot uses RAG to answer product questions. It retrieves the latest specs from Scavio's Google search results and combines them with internal documentation before generating a response, ensuring accuracy without retraining.
Platforms
Retrieval-Augmented Generation (RAG) is relevant across the following platforms, all accessible through Scavio's unified API:
- YouTube
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