Recent Developments in Retrieval-Augmented Generation for Scientific Applications

Comprehensive overview of recent advances in Retrieval-Augmented Generation (RAG) systems, with emphasis on scientific and medical applications. Discussed state-of-the-art retrieval methods including dense retrievers, hybrid search, and learned sparse representations. Covered generation models fine-tuned for domain-specific knowledge synthesis. Presented case studies on applying RAG to medical diagnosis support, drug discovery literature mining, and scientific paper summarization. Explored challenges including retrieval quality, hallucination mitigation, and factual consistency. Demonstrated practical implementations using vector databases and efficient retrieval architectures for real-time knowledge-intensive tasks.