RAGulator: Tackling Out-of-Context Text in RAG Systems

In this episode, we explore RAGulator, a lightweight model designed to detect out-of-context (OOC) text in retrieval-augmented generation (RAG) systems. Learn how RAGulator uses existing datasets to simulate OOC and in-context scenarios, and how fine-tuned BERT-based classifiers and ensemble meta-classifiers play a role in its success. We discuss its superior performance compared to larger language models, particularly in speed and resource efficiency, and why it’s a game-changer for enterprise applications. Join us for insights into making RAG systems more reliable and efficient.

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