Smart Tourist Safety Monitoring Using Iot And Artificial Intelligence: A Review Of Location Tracking, Risk Detection And Emergency Response

The rapid growth of digital documents creates a need for systems that can find relevant information and answer questions accurately. Traditional question-answering systems rely on predefined knowledge bases or keyword search and may fail to understand the meaning of complex questions, while large language models alone can produce fluent but unsupported answers. Retrieval-Augmented Generation (RAG) combines information retrieval with the generation abilities of large language models. This paper reviews RAG for document question answering. It explains sparse retrieval with BM25, dense retrieval with dual encoders and sentence embeddings, approximate nearest-neighbour vector search, re-ranking, and the integration of retrieved passages into generation, including fusion-in-decoder and self-reflective retrieval. Practical issues such as chunking, context length and the tendency of models to overlook information in the middle of long contexts are discussed, together with evaluation of faithfulness and relevance. A five-layer system is proposed in which users upload PDF documents that are extracted, cleaned and chunked, embedded with a sentence-transformer model and stored in a vector database; queries retrieve relevant chunks that are passed to an LLM to generate grounded answers through a Streamlit interface. Research gaps, objectives and a comparison of retrieval methods are presented.