GenAI for Product Managers: Model Selection, Prompt Engineering, Integration, Performance Analysis, and Risk Mitigation
A comprehensive course equipping product managers with the skills to leverage GenAI for innovative and responsible product development.
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GenAI Fundamentals for Product Managers
Unit 1: Introduction to GenAI
What is GenAI?
GenAI & PM: A Perfect Fit
LLMs: The Language Wizards
Diffusion Models: The Artists
Other GenAI Models
Unit 2: The GenAI Product Lifecycle
Ideation with GenAI
Prototyping with GenAI
Development with GenAI
Testing with GenAI
Deployment and GenAI
Unit 3: Stakeholders & Ethics
Key Stakeholders
Ethical Considerations
Risk Mitigation
Regulations & Compliance
Model Selection and Evaluation
Unit 1: Defining Model Selection Criteria
Intro to Model Selection
Cost Considerations
Performance Metrics
Accuracy Demystified
Latency Matters
Unit 2: Model Evaluation Techniques
Benchmarks Overview
A/B Testing GenAI
Human Evaluation
Bias Detection
The Art of Error Analysis
Unit 3: Model Architectures and Deployment
LLMs vs. Diffusion Models
Cloud vs. On-Premise
Edge Deployment
Documenting Decisions
Prompt Engineering for Product Features
Unit 1: Fundamentals of Prompt Engineering
What is Prompt Engineering?
Anatomy of a Prompt
Prompting Best Practices
Prompting Pitfalls to Avoid
Unit 2: Advanced Prompting Techniques
Few-Shot Learning
Chain-of-Thought Prompting
Prompt Chaining
Zero-Shot Prompting
Unit 3: Prompting for Product Features
Content Generation Prompts
Summarization Prompts
Personalization Prompts
Classification Prompts
Unit 4: Prompt Management and Iteration
Prompt Version Control
Prompt A/B Testing
Integrating GenAI into Product Workflows
Unit 1: GenAI Integration Fundamentals
Intro to GenAI APIs/SDKs
Choosing the Right API
Setting Up Your Environment
Basic API Calls
Unit 2: UI/UX for GenAI Features
GenAI-Powered UI Design
Handling GenAI Latency
Presenting GenAI Results
User Feedback Loops
Unit 3: Data Privacy and Security
Data Privacy Basics
Secure API Integration
Data Storage Security
Compliance Checklist
Unit 4: Optimization and Error Handling
Performance Optimization
Scalability Strategies
Handling API Errors
Performance Analysis and Optimization
Unit 1: Defining KPIs for GenAI Features
Intro to GenAI KPIs
Engagement KPIs
Task Completion KPIs
Quality & Accuracy KPIs
Unit 2: Measuring and Analyzing Performance
Setting Up Measurement
Analyzing the Data
User Feedback Analysis
Unit 3: Optimization and A/B Testing
Identifying Improvements
A/B Testing Basics
Testing Model Variations
Prompt Engineering A/B
Unit 4: Continuous Monitoring and Refinement
Setting Up Monitoring
Refinement Strategies
The Refinement Cycle
Risk Mitigation and Responsible AI
Unit 1: Understanding GenAI Risks and Biases
GenAI Risk Landscape
Bias in GenAI: An Overview
Privacy Concerns
Security Vulnerabilities
Unit 2: Mitigation Strategies and Responsible AI Practices
Fairness Metrics
Bias Mitigation Techniques
Data Governance
Transparency and Explainability
Human-in-the-Loop
Unit 3: Compliance, Monitoring, and Future Trends
Regulatory Landscape
Monitoring for Harm
Auditing GenAI Systems
Future of Responsible AI
Case Studies