Welcome to the fifth part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In this part, you will learn critical skills for ensuring reliable generative AI outputs. You'll learn to build evaluation frameworks measuring relevance and accuracy, implement testing methodologies like A/B testing and multi-model comparison, and develop user-centered quality assurance processes. The curriculum covers troubleshooting techniques for AI-specific
Gen AI Dev- Implement Evaluation Systems for Generative AI

Gen AI Dev- Implement Evaluation Systems for Generative AI
This course is part of AWS Generative AI Developer Advanced Professional Certificate

Instructor: AWS Instructor
Access provided by University of Split, Faculty of Economics, Business and Tourism
Recommended experience
What you'll learn
Assess generative AI outputs using metrics for relevance, factual accuracy, consistency, and fluency
Design quality assurance processes integrating continuous evaluation workflows and regression testing
Apply testing methods including A/B testing and multi-model comparison for output quality
Skills you'll gain
- Artificial Intelligence
- Machine Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Verification And Validation
- User Acceptance Testing (UAT)
- MLOps (Machine Learning Operations)
- Performance Measurement
- Generative AI Agents
- Cloud Computing
- System Monitoring
- Software Development
- Software Quality Assurance
- Development Testing
- Quality Assurance
- Large Language Modeling
- Natural Language Processing
Tools you'll learn
Details to know

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September 2026
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There are 5 modules in this course
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