{"id":11392,"date":"2026-08-10T10:31:36","date_gmt":"2026-08-10T10:31:36","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/coursera-retrieval-augmented-generation-rag-2025-7\/"},"modified":"2026-08-10T10:31:36","modified_gmt":"2026-08-10T10:31:36","slug":"coursera-retrieval-augmented-generation-rag-2025-7","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/coursera-retrieval-augmented-generation-rag-2025-7\/","title":{"rendered":"Coursera \u2013 Retrieval Augmented Generation (RAG) 2025-7"},"content":{"rendered":"
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Descriptions<\/h2>\n

Retrieval Augmented Generation (RAG), Retrieval Augmented Generation (RAG) improves large language model (LLM) responses by retrieving relevant data from knowledge bases\u2014often private, recent, or domain-specific\u2014and using it to generate more accurate, grounded answers. In this course, you\u2019ll learn how to build RAG systems that connect LLMs to external data sources. You\u2019ll explore core components like retrievers, vector databases, and language models, and apply key techniques at both the component and system level. Through hands-on work with real production tools, you\u2019ll gain the skills to design, refine, and evaluate reliable RAG pipelines\u2014and adapt to new methods as the field advances. Across five modules, you\u2019ll complete hands-on programming assignments that guide you through building each core part of a RAG system, from simple prototypes to production-ready components. You\u2019ll apply your skills using real-world data from domains like media, healthcare, and e-commerce. By the end of the course, you\u2019ll combine everything you\u2019ve learned to implement a fully functional, more advanced RAG system tailored to your project\u2019s needs.<\/p>\n

What you\u2019ll learn<\/h3>\n