RAG
IntermediateArchitecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce hallucinations.
AdvertisementAd space — term-top
Why It Matters
RAG is crucial because it enhances the accuracy and relevance of AI-generated content, making it particularly valuable in applications like search engines, customer support, and educational tools. By grounding responses in real data, RAG helps mitigate the risk of misinformation and improves user trust in AI systems.
Retrieval-Augmented Generation (RAG) is an architectural framework that combines retrieval mechanisms with generative models to enhance the quality and relevance of generated content. This approach involves retrieving pertinent documents or data from a vector database, which are then used to condition the generative process of the model. Mathematically, RAG can be expressed as a two-step process: first, a retrieval function identifies relevant documents based on embedding similarity, and second, a generative model produces output conditioned on these documents. This architecture effectively reduces hallucinations by grounding the generation process in real, contextually relevant information, thereby improving the overall reliability and accuracy of the model's outputs.