MODULE 3

Generative AI & Large Language Models

Master the technology behind ChatGPT, Claude, and other generative AI systems. Understand how they work, how to use them effectively, and how to manage their risks.

6
Parts
5-6
Hours
30
Quiz Questions

Learning Objectives

Upon completing this module, you will be able to:

◈ Explain what generative AI is and how it differs from traditional AI
◈ Describe how large language models work at a conceptual level
◈ Understand LLM training, fine-tuning, and alignment techniques
◈ Apply prompt engineering best practices
◈ Identify and mitigate GenAI risks including hallucinations
◈ Evaluate enterprise GenAI deployment patterns
1

The Generative AI Revolution

Explore the technologies behind generative AI: GANs, VAEs, diffusion models, and foundation models that power the current AI revolution.

⏱ 40-50 min ☆ Technology
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2

Large Language Models Explained

Understand transformers, attention mechanisms, and tokenization - the core concepts that make modern language models possible.

⏱ 50-60 min ☆ Technical
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3

LLM Training & Fine-Tuning

Learn about pre-training, RLHF alignment, and parameter-efficient fine-tuning techniques like LoRA.

⏱ 45-55 min ☆ Training
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4

Prompt Engineering & Best Practices

Master the art of communicating effectively with LLMs: design principles, few-shot learning, and advanced prompting techniques.

⏱ 45-55 min ☆ Practical
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5

GenAI Risks & Mitigations

Understand hallucinations, bias, copyright concerns, and other risks unique to generative AI systems.

⏱ 50-60 min ☆ Risk
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6

Enterprise GenAI Deployment

Explore RAG architectures, vector databases, security considerations, and patterns for enterprise-scale GenAI implementation.

⏱ 50-60 min ☆ Enterprise
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Module 3 Assessment

Test your understanding with 30 questions covering all topics from this module. You need 80% to pass.

Start Quiz →