The paper introduces RankRAG, a novel instruction fine-tuning framework designed to enhance the retrieval-augmented generation (RAG) capabilities of large language models (LLMs). RankRAG aims to improve both context ranking and answer generation by instruction-tuning a single LLM. The method involves two stages: supervised fine-tuning (SFT) and unified instruction-tuning for ranking and generation. During training, the LLM is trained on a blend of high-quality instruction-following datasets, context-rich QA data, retrieval-augmented QA data, context ranking data, and retrieval-augmented ranking data. This approach effectively enhances the LLM's ability to filter out irrelevant contexts and improve the quality of generated answers. Experimental results on various knowledge-intensive NLP tasks, including open-domain QA, fact verification, and conversational QA, demonstrate that RankRAG outperforms existing RAG methods and state-of-the-art models, such as ChatQA-1.5, on multiple benchmarks. RankRAG also shows superior performance in challenging datasets and domain-specific tasks, highlighting its adaptability and generalization capabilities.The paper introduces RankRAG, a novel instruction fine-tuning framework designed to enhance the retrieval-augmented generation (RAG) capabilities of large language models (LLMs). RankRAG aims to improve both context ranking and answer generation by instruction-tuning a single LLM. The method involves two stages: supervised fine-tuning (SFT) and unified instruction-tuning for ranking and generation. During training, the LLM is trained on a blend of high-quality instruction-following datasets, context-rich QA data, retrieval-augmented QA data, context ranking data, and retrieval-augmented ranking data. This approach effectively enhances the LLM's ability to filter out irrelevant contexts and improve the quality of generated answers. Experimental results on various knowledge-intensive NLP tasks, including open-domain QA, fact verification, and conversational QA, demonstrate that RankRAG outperforms existing RAG methods and state-of-the-art models, such as ChatQA-1.5, on multiple benchmarks. RankRAG also shows superior performance in challenging datasets and domain-specific tasks, highlighting its adaptability and generalization capabilities.