1. Introduction to Generative AI
- Overview of Generative AI Foundations
- Importance of Generative AI for CTOs
2. Generative AI Algorithms Demystified
- Introduction to Large Language Models (LLMs)
- Different types of AI: Narrow-purpose vs. General-purpose
- Balancing intent alignment in AI models
3. Opportunities with Generative AI
- Identifying opportunities for AI applications
- Labeling, training, and deploying AI solutions
- Narrow-purpose vs. General-purpose AI models
4. Custom Comparison and Custom Demonstration
- Evaluating generative AI solutions
- Conducting custom comparisons and demonstrations
- Frameworks to assess AI solutions
5. Environments for Generative AI
- Understanding different environments for AI solutions
- Proprietary, Open-source, Cloud, On-prem, Edge
- Prompt Engineering, Plugins, and Model Fine-tuning
6. Large Language Models (LLMs)
- Overview of LLMs like GPT-4, LaMDA, PaLM, Alpaca
- Comparing LLMs with Chatbots
- LLM Leaderboard and advancements in the field
7. From Data to ChatGPT
- ML training process for LLMs
- Base LLM and Chatbot models
- Training and inference steps for LLMs
8. AI Intent Alignment and Risk Management
- Understanding intent alignment in AI models
- Risk management techniques for safe AI solutions
- Addressing biases and behavioral issues in AI models
9. Evaluation Frameworks for Generative AI
- Frameworks to evaluate and benchmark AI solutions
- Importance of human evaluations and limitations
- Benchmarking, red teaming, and risk analysis
10. Generative AI MLOps
- Overview of MLOps for generative AI
- Development, deployment, and maintenance lifecycle
- Monitoring, retraining, and risk control mechanisms