Speculative RAG introduces a novel framework that leverages larger language models to optimize retrieval augmented generation, reducing latency while improving accuracy.

• Uses a parallel drafting approach to efficiently verify multiple RAG drafts simultaneously
• Achieves 1.5-2x faster inference compared to traditional RAG methods
• Demonstrates significant performance improvements across multiple benchmarks including medical datasets
• Maintains accuracy without requiring additional fine-tuning

For medical applications, Speculative RAG provides more reliable and up-to-date information retrieval, critical for clinical decision support and medical research where factual accuracy and completeness are paramount.

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting