RAG Based

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge sources to improve accuracy and reduce hallucinations. Current research focuses on improving RAG pipeline efficiency and robustness, including developing automated evaluation methods and exploring various knowledge base types (e.g., API documentation, web data, knowledge graphs) and their impact on performance. This active area of research is crucial for building reliable and trustworthy AI systems across diverse applications, from question answering and test generation to personalized learning and medical error correction.