提示词与上下文工程
甄选并验证：Leena Shah, Applied AI Engineer, Zoom任何人都可以从语言模型中诱出一个令人印象深刻的答案。但获得可靠的答案——在混乱的真实输入上，规模化地，当错误答案确实会造成损失时——是一项不同的技能，几乎没有人教过。 这门课程就是关于这项技能的。你将学习从真实组件构建提示，而不是借用咒语，设计模型所需的上下文——正确的文档、示例、工具和记忆，且不增加噪音——然后做大多数人完全跳过的事情：测量。黄金数据集、确定性检查、针对人类标签校准的LLM评判器、回归套件、真正适合RAG和代理系统的指标。这个循环告诉你改变是让事情变得更好还是只是不同。 它基于严肃的实践者和主要模型提供商今天的工作方式，而不是随着下一个版本就失效的民间传说。这里有代码形式的工件——模式、工具定义、断言——但你不会编写任何代码，技术与非技术学习者遵循相同的路径。通过三十六节课，你将从头解模型行为到完成可重复的工程循环，并沿途掌握任何真实系统所需的安全和提示注入知识。 停止构建只工作一次的演示。开始构建能够持久运行的东西。
课程目录
关于课程作者
Leena Shah’s portfolio reads less like a catalog of models than a record of bottlenecks removed: customer-support teams searching thousands of documents, analysts waiting on manual summaries, and product managers unsure whether an AI answer can be trusted. She has addressed those problems with retrieval-augmented assistants, multimodal extraction, voice agents, and tool-using workflows, pairing Python and FastAPI services with vector databases, cloud infrastructure, and evaluation suites that measure grounding, latency, safety, and cost. Leena has also fine-tuned open models for private environments, introduced human-review loops and production guardrails, and worked alongside product, security, and domain specialists to turn rough prototypes into monitored features. Her strength lies in recognizing where AI can genuinely improve a workflow—and engineering the surrounding system so that improvement survives contact with real users.
评价 (7)
- sturdy_barista
moves too fast
- sunny_pilot
great course!
- canny_beaver
thx, super helpful
- lunar_camel
too basic
- wily_ocelot
expected more