Generate Smarter Generative AI Outputs course Syllabus

Overview: This career-focused program teaches learners how to optimize and deploy high-quality generative AI solutions using Google Cloud tools. The curriculum covers foundational concepts, advanced prompting, grounding techniques, and safety evaluation. With approximately 8–12 weeks of total commitment, each module combines theory and hands-on labs to build practical skills in real-world AI applications.

Module 1: Foundations of Generative AI

Estimated time: 10 hours

• Understand how large language models (LLMs) and diffusion models work
• Learn the basics of prompt design and tokenization
• Explore use cases of generative AI in business and technology
• Develop familiarity with cloud-based AI platforms

Module 2: Advanced Prompt Engineering

Estimated time: 10 hours

• Design structured prompts for consistent outputs
• Apply few-shot and zero-shot prompting techniques
• Control tone, format, and response constraints
• Evaluate model outputs for accuracy and relevance

Module 3: Grounding and Retrieval-Augmented Generation (RAG)

Estimated time: 10 hours

• Understand how RAG improves factual accuracy
• Learn to connect models with external data sources
• Study embeddings and vector search integration
• Reduce hallucinations through structured grounding

Module 4: Safety, Evaluation, and Optimization

Estimated time: 10 hours

• Implement content moderation and bias controls
• Understand model evaluation metrics
• Optimize performance, latency, and cost
• Deploy scalable generative AI applications

Module 5: Practical Cloud-Based Implementation

Estimated time: 12 hours

• Build generative AI applications using Google Cloud tools
• Test and refine outputs in real-world scenarios
• Improve reliability and scalability of AI solutions

Module 6: Final Project

Estimated time: 16 hours

• Design a production-level generative AI application
• Apply advanced prompting, RAG, and grounding techniques
• Submit for peer review with performance and safety evaluation

Prerequisites

• Basic understanding of artificial intelligence concepts
• Familiarity with cloud computing fundamentals
• Access to Google Cloud Platform for lab exercises

What You'll Be Able to Do After

• Optimize generative AI outputs using advanced prompt engineering
• Integrate retrieval-augmented generation (RAG) for improved accuracy
• Apply grounding techniques to reduce hallucinations
• Evaluate and deploy reliable, scalable AI applications
• Implement safety controls and performance optimization in production environments