RankRAG is a novel framework that enhances the retrieval-augmented generation (RAG) capability of large language models (LLMs) by simultaneously instructing the LLM on context ranking and answer generation. The framework introduces a two-stage instruction-tuning process, where the first stage involves supervised fine-tuning (SFT) on high-quality instruction-following datasets, and the second stage incorporates context-rich QA, retrieval-augmented QA, and ranking datasets to improve the LLM's ability to filter out irrelevant contexts during both retrieval and generation. RankRAG outperforms existing expert ranking models and strong baselines, including GPT-4 and ChatQA-1.5, on nine knowledge-intensive benchmarks. It also performs comparably to GPT-4 on five biomedical benchmarks without instruction fine-tuning on biomedical data, demonstrating its strong generalization capability. The framework includes a retrieve-rerank-generate pipeline, where the LLM reranks retrieved contexts to select the most relevant ones before generating the final answer. RankRAG is data-efficient and time-efficient, achieving strong performance with a modest amount of ranking data and maintaining adaptability across various tasks. The model is robust to the choice of retrievers and shows significant improvements on challenging datasets, such as long-tailed and multi-hop QA tasks. The results demonstrate that RankRAG is a powerful and versatile framework for RAG tasks, capable of adapting to new domains without additional post-training.RankRAG is a novel framework that enhances the retrieval-augmented generation (RAG) capability of large language models (LLMs) by simultaneously instructing the LLM on context ranking and answer generation. The framework introduces a two-stage instruction-tuning process, where the first stage involves supervised fine-tuning (SFT) on high-quality instruction-following datasets, and the second stage incorporates context-rich QA, retrieval-augmented QA, and ranking datasets to improve the LLM's ability to filter out irrelevant contexts during both retrieval and generation. RankRAG outperforms existing expert ranking models and strong baselines, including GPT-4 and ChatQA-1.5, on nine knowledge-intensive benchmarks. It also performs comparably to GPT-4 on five biomedical benchmarks without instruction fine-tuning on biomedical data, demonstrating its strong generalization capability. The framework includes a retrieve-rerank-generate pipeline, where the LLM reranks retrieved contexts to select the most relevant ones before generating the final answer. RankRAG is data-efficient and time-efficient, achieving strong performance with a modest amount of ranking data and maintaining adaptability across various tasks. The model is robust to the choice of retrievers and shows significant improvements on challenging datasets, such as long-tailed and multi-hop QA tasks. The results demonstrate that RankRAG is a powerful and versatile framework for RAG tasks, capable of adapting to new domains without additional post-training.