Did you know that 85% of organizations deploying generative AI systems experience significant performance degradation within the first six months due to inadequate monitoring and governance? As AI becomes mission-critical for business operations, the ability to maintain consistent, high-quality outputs while managing risks has become one of the most sought-after skills in the industry.
GenAI Prompting, Evaluation, and Governance
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GenAI Prompting, Evaluation, and Governance
This course is part of multiple programs.
Instructor: Hurix Digital
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Performance monitoring is essential for maintaining AI system reliability and fairness across diverse user populations
Technical architecture decisions (fine-tuning vs RAG) require systematic evaluation of costs, capabilities, and maintenance requirements
Effective AI governance requires proactive policy creation, technical guardrails, and cross-functional collaboration to ensure responsible deployment
Sustainable AI operations depend on establishing measurable quality benchmarks and continuous feedback loops
Skills you'll gain
- Data-Driven Decision-Making
- Large Language Modeling
- Cross-Functional Team Leadership
- AI Security
- Gap Analysis
- Performance Analysis
- Generative AI
- Governance
- Performance Metric
- Prompt Engineering
- System Monitoring
- Responsible AI
- Content Performance Analysis
- Quality Assessment
- Retrieval-Augmented Generation
- Model Evaluation
- Cost Benefit Analysis
- Risk Management
- Compliance Management
- Governance Risk Management and Compliance
Details to know
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Recently updated!
December 2025
Taught in English
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There are 3 modules in this course
Learners will master systematic cohort-based analysis techniques to detect and diagnose AI performance drift patterns that aggregate metrics often conceal.
What's included
3 videos1 reading2 assignments
Learners will systematically evaluate architectural trade-offs between fine-tuning and retrieval-augmented generation approaches to make data-driven decisions for domain-specific AI implementations.
What's included
3 videos2 readings2 assignments
Learners will design comprehensive governance frameworks with enforceable policies and technical guardrails that ensure responsible AI deployment while enabling enterprise innovation.
What's included
2 videos2 readings3 assignments
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