Generative AI for Beginners

A comprehensive curriculum exploring the fundamentals of Generative AI, Large Language Models, prompt engineering, and the development of AI-powered applications using tools like Azure OpenAI and the Power Platform.

Master the fundamentals of Generative AI and build intelligent applications from scratch.

Acknowledgments: Microsoft, Azure, and OpenAI.

1. Explain the mechanical inner workings of LLMs, including tokenization, the attention mechanism, and non-deterministic output.
2. Compare various LLM categories (Foundation Models, Open-source vs. Proprietary, and Encoder/Decoder architectures) to select the right tool for a business scenario.
3. Evaluate strategies for improving model results, specifically choosing between Prompt Engineering, Retrieval Augmented Generation (RAG), and Fine-tuning.
4. Define Prompt Engineering and explain its role as the primary programming interface for generative AI.
5. Differentiate between Base LLMs and Instruction-Tuned LLMs and how they process tokens.
6. Construct complex prompts using instructions, primary content, cues, and templates.
7. Construct and configure text generation applications using the openai library, managing environment variables, and adjusting output variety via temperature.
8. Differentiate between rule-based chatbots and context-aware generative AI applications while implementing Microsoft’s Six Principles of Responsible AI.
9. Execute semantic search by converting text into embeddings (vectors) and applying cosine similarity to find relevant content beyond simple keyword matching.
10. Build and configure image generation applications while implementing "meta prompts" to define content boundaries and safety.