TL;DR
An advanced RAG framework that uses a small specialist language model to quickly draft multiple candidate answers, which are then verified and refined by a larger generalist model.
Rather than feeding a massive set of retrieved documents directly to a large language model, Speculative RAG splits the workload. A smaller, specialized RAG model processes distinct subsets of documents in parallel to generate multiple quick drafts and reasoning pathways. The primary, larger model then evaluates and refines these pre-drafted candidates to select the final response, drastically reducing input context size and compute overhead.
Why this matters for your business
It reduces latency by up to 50% and improves accuracy on complex, knowledge-intensive tasks, making real-time RAG feasible for larger-scale enterprise deployments.
