What You Need to Know Before
You Start
Starts 6 June 2025 20:55
Ends 6 June 2025
00
days
00
hours
00
minutes
00
seconds
50 minutes
Optional upgrade avallable
Not Specified
Progress at your own speed
Free Video
Optional upgrade avallable
Overview
Explore modern evaluation techniques for AI agents, including LLM-as-judge, code-based methods, and human feedback, with practical demonstrations using Arize Phoenix for effective agent assessment.
Syllabus
- Introduction to AI Agent Evaluation
- Modern Evaluation Techniques Overview
- LLM-as-Judge Evaluation
- Code-Based Evaluation Methods
- Human Feedback Mechanisms
- Practical Sessions with Arize Phoenix
- Case Studies and Real-World Applications
- Future Trends in AI Agent Evaluation
- Conclusion and Takeaways
Overview of AI agents and their roles
Importance of evaluation in AI development
Classifying evaluation techniques
Choosing the right evaluation method
Explanation of LLM-as-judge
Advantages and limitations
Practical demo using Arize Phoenix
Automated testing frameworks
Performance metrics and benchmarking
Code-based case studies
Gathering qualitative feedback
Designing user studies for AI
Integrating human feedback into agent improvement
Introduction to Arize Phoenix platform
Hands-on exercises: Setting up evaluations
Analyzing results and generating insights
Review of successful AI agent evaluations
Lessons learned from real-world projects
Emerging techniques and technologies
Predicting challenges and opportunities in evaluation
Summary of key techniques learned
Strategies for continuous evaluation improvement
Subjects
Computer Science
