This course includes lecture materials, visual presentation, and audience participation.

Learning Objectives

• Identify the fundamental concepts of FMs and LLMs
• Define prompt engineering and identify the best practices for designing effective prompts
• Identify the basic types of prompt techniques, including zero-shot, few-shot, and CoT  techniques

Intended audience

• Prompt engineers
• Data scientists        
• Developers

Event outline

• Module 1: Foundation models and large language model
• How does a foundation model function?
• Training FMs
• Types of FMs  
• Large language models   
• Transformer architecture
• Neural networks
• Module 2: Key concepts of prompt engineering
• Fine-tuning and prompt engineering
• Elements of a prompt
• Best practices for designing effective prompts
• Practice with prompts   
• Module 3: Basic prompt techniques
• Zero-shot prompting
• Few-shot prompting
• Chain-of-thought prompting
• Conclusion

Module descriptions

Module 1: Foundation models and large language models

In this module, you will develop a fundamental understanding of FMs, including an understanding of a subset of FMs called LLMs. First, you will be introduced to the basic concepts of a foundation model, such as self-supervised learning and fine-tuning. Next, you will learn about two types of FMs: text-to-text models and text-to-image models. Finally, you will learn about LLMs' functionality and use cases, the subset of foundation models that most often utilize prompt engineering.

Module 2: Key concepts of prompt engineering

In this module, you are introduced more fully to prompt engineering, the set of practices that focus on developing, designing, and optimizing prompts to enhance the output of FMs for your specific business needs. Then, you learn about the different elements of a prompt. Finally, the module provides a list of general best practices for designing effective prompts, and you can participate in voting for which prompts showcase those best practices.

Module 3: Basic prompt techniques

In this module, you will learn about basic prompt engineering techniques that can help you effectively use generative AI applications for your unique business objectives. First, the module defines zero-shot and few-shot prompting techniques. Then, it defines CoT prompting, the building block for
several advanced prompting techniques. This module provides tips and examples of each type of prompt. <

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