Python shines in RAG (Retrieval-Augmented Generation) systems due to its efficiency in orchestrating various processes and its extensive libraries, such as LangChain and Hugging Face Transformers. The building blocks for RAG include data extraction and preprocessing, transforming data into vectors via embedding models, and using vector databases for retrieval. Python excels in setting up data pipelines for indexing, retrieval, and generation, integrating different components, and ensuring low-latency, high-efficiency real-time processing. Real-world applications of RAG systems showcase Python's benefits and challenges in implementation, demonstrating its versatility and robustness in managing complex data flows and interactions.