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
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Safeguard LLM Outputs: Test and Evaluate
This course is part of LLM Optimization & Evaluation Specialization
Instructor: LearningMate
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Intermediate level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Build and validate a robust safety testing framework for LLMs. Create behavioral test suites and use mutation testing to ensure their effectiveness.
Skills you'll gain
- AI Security
- Test Script Development
- Verification And Validation
- Unit Testing
- Software Technical Review
- Large Language Modeling
- Test Case
- Security Testing
- Quality Assessment
- Penetration Testing
- Prompt Engineering
- Responsible AI
- Threat Modeling
- Software Testing
- API Testing
- Maintainability
- Test Tools
- Model Evaluation
- Code Coverage
- LLM Application
Details to know
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Recently updated!
December 2025
Taught in English
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This course is part of the LLM Optimization & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
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.
What's included
4 videos2 readings3 assignments2 ungraded labs
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