With AI spending projected to reach $300B by 2026 and 77% of enterprises already exploring GenAI applications, understanding how to effectively build and deploy these solutions is no longer optional. This comprehensive 3-hour course unveils a proven 4-step framework for creating production-ready GenAI applications. Perfect for investors evaluating AI opportunities, developers building real-world solutions, and tech leaders crafting AI strategies.

Module 1: Foundations of Generative AI

- What is GenAI and why it matters now

- Understanding the AI technology stack: Infrastructure, Models, and Agents

- Key components: Transformers, LLMs, and Neural Networks

- Non-deterministic nature of GenAI and its implications

- Evolution from traditional ML to modern GenAI

Module 2: The 4-Step Framework for GenAI Applications: Use case and Models

Step 1: Define Your Use Case

- Problem identification and scoping

- Evaluating AI readiness

- Real-world application patterns

Step 2: Select Your Model

- Pre-trained vs custom models

- Model sizing and quantization

- Infrastructure requirements

- Cost-performance tradeoffs

Module 3: The 4-Step Framework for GenAI Applications: Adapt and Deploy

Step 3: Adjust and Adapt

- Fine-tuning strategies

- Prompt engineering techniques

- Human feedback loops

- Responsible AI

- Evaluation methods

Step 4: Integration and Deployment

- RAG framework implementation

- Vector databases

- External data sources

- Production considerations

Module 4: Building AI Agents

- Understanding AI Agents and workflows

- Popular frameworks overview (LangChain, AutoGen, etc.)

- Real-world integration patterns

- Best practices for production deployment