| | 資料類型 | 狀態 | 應還日期 | 預約人數 | 館藏地 | 索書號 | 條碼號 | | 找書 | 圖書 | 在架上 | | 0 | 總館
西文圖書區  | QA76.9.N38 .P65 2026 | W116359 |
| 內容簡介 | As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents. Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve. Learn core RAG components including embedding, retrieval, and generation techniques Understand advanced workflows like semantic-aware chunking and multi-query prompting Build custom solutions such as chatbots and autonomous agents for specific data challenges Continuously evaluate and optimize systems for accuracy, relevance, and performance. |
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