As AI models like Google's Gemini have shown, even the most advanced systems can have spectacular safety failures, leading to brand damage and a loss of user trust. "Safeguard LLM Outputs: Test and Evaluate" is an intermediate course for developers and ML engineers who need to move beyond functional testing and build truly trustworthy AI. This course teaches you the rigorous, adversarial testing methodologies that professional AI Red Teams use to secure high-stakes applications.
Safeguard LLM Outputs: Test and Evaluate
只需 199 美元（原价 399 美元）即可通过 Coursera Plus 学习更高水平的技能。立即节省
Safeguard LLM Outputs: Test and Evaluate
本课程是 LLM Optimization & Evaluation 专项课程 的一部分
位教师：LearningMate
包含在 中
您将学到什么
Build and validate a robust safety testing framework for LLMs. Create behavioral test suites and use mutation testing to ensure their effectiveness.
您将获得的技能
- Software Testing
- API Testing
- Code Coverage
- Unit Testing
- Prompt Engineering
- Penetration Testing
- Test Script Development
- AI Security
- LLM Application
- Software Technical Review
- Verification And Validation
- Quality Assessment
- Large Language Modeling
- Responsible AI
- Maintainability
- Threat Modeling
- Security Testing
- Model Evaluation
- Test Tools
- Test Case
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 LLM Optimization & Evaluation 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有1个模块
This comprehensive module takes learners through the end-to-end process of creating and validating a safety testing framework for LLM applications. You will first build a behavioral test suite to enforce safety policies and then "test their tests" using mutation testing to find and fix hidden weaknesses, ensuring the safety net is truly robust.
涵盖的内容
4个视频2篇阅读材料3个作业2个非评分实验室
获得职业证书
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位教师
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