Retrieval-augmented generation (RAG) techniques enhance the capabilities of large language models (LLMs) by incorporating external knowledge sources, greatly improving performance, particularly in tasks that require accurate and specialized information. This article outlines six distinct types of RAG, including traditional RAG, Graph Retrieval, Knowledge-Augmented Generation, Cache-Augmented Generation, Zero-Indexing Internet Search, and Corrective Retrieval techniques. As AI features grow in importance for web apps, understanding these RAG types is crucial for frontend developers, enabling better collaboration with backend engineers to create advanced user interactions utilizing real-time, contextual data.
Retrieval-Augmented Generation (RAG) techniques enhance LLMs by integrating external knowledge sources, which improves their performance in tasks requiring up-to-date or specialized information.
The six RAG types include RAG, Graph RAG, Knowledge-Augmented Generation, Cache-Augmented Generation, Zero-Indexing Internet Search-Augmented Generation, and Corrective Retrieval-Augmented Generation, helping to improve LLMs.
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