GenAI for ML Product Entrepreneurs: Model Selection, Prompt Engineering, Integration, Fine-tuning, and Responsible AI
Master GenAI model selection, prompt engineering, integration, fine-tuning, and responsible AI to build innovative upskilling products.
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GenAI Model Selection and Evaluation
Unit 1: GenAI Model Landscape
GenAI Model Overview
LLMs for Upskilling
Diffusion Models
Unit 2: KPIs for GenAI in Upskilling
Defining Key KPIs
Measuring Accuracy
Measuring Relevance
Measuring Engagement
Unit 3: Framework for Model Comparison
Cost-Performance Tradeoffs
Ethical Considerations
Integration Complexity
Prompt Engineering for Upskilling
Unit 1: Prompt Engineering Fundamentals
Intro to Prompting
Prompting Best Practices
Prompting Frameworks
Unit 2: Prompting for Specific Upskilling Use Cases
Personalized Learning
Interactive Exercises
Assessment Questions
Content Generation
Unit 3: Advanced Prompt Engineering Techniques
Controlling Style & Tone
Iterative Prompt Refinement
Prompt Engineering Tools
Integration and Customization of GenAI Models
Unit 1: GenAI Integration Fundamentals
Intro to GenAI APIs
API Keys & Security
Unit 2: Retrieval-Augmented Generation (RAG)
RAG: The Big Picture
Vector Databases for RAG
Implementing RAG
Unit 3: Fine-Tuning GenAI Models
Fine-Tuning Explained
Preparing Data
Fine-Tuning Techniques
Unit 4: Scalability, Security, and Maintainability
Scaling GenAI
Security Best Practices
Responsible AI in Upskilling
Unit 1: Understanding Bias and Fairness
Sources of Bias in GenAI
Measuring Bias
Mitigating Data Bias
Unit 2: Data Privacy and Security
Data Privacy Principles
Security Protocols
Anonymization Techniques
Unit 3: Content Moderation and Ethical Guidelines
Content Moderation
Ethical Guidelines
Transparency and Explainability
Feedback Mechanisms