Generative AI Engineering Tutorial

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Course Contents

Generative AI Foundations

• Generative AI and Large Language Models
• Tokens, Context Windows, and Sampling
• Choosing a Model for a Task

Prompt Design

• Instructions, Context, and Constraints
• Few-Shot Examples and Structured Output
• Prompt Injection and Instruction Boundaries

Embeddings and Retrieval

• Embeddings and Semantic Similarity
• Chunking and Document Preparation
• Vector Search and Hybrid Retrieval

Retrieval-Augmented Generation

• RAG Architecture
• Reranking and Context Assembly
• Grounded Answers and Citations

AI Application Patterns

• Designing an AI Service
• Streaming Responses
• Conversation Memory

Tools and Agents

• Tool Calling
• Agent Loops and State Machines
• Sandboxing and Human Approval

Evaluation and Safety

• Building an Evaluation Dataset
• Scoring Quality and Groundedness
• Guardrails and Red Teaming

Production AI Engineering

• Cost, Latency, Caching, and Fallbacks
• Observability, Privacy, and Versioning
• Capstone: Build a Cited Knowledge Assistant