Ementorhub

What you'll learn

Explain the concept, relevance, and best practices of prompt engineering to guide generative AI models in producing meaningful, accurate outputs.

Apply prompt engineering techniques to text prompts, improving the reliability and quality of large language models.

Practice prompt engineering techniques and approaches, including interview pattern, chain-of-thought, tree-of-thought, to improve prompt outcomes.

Explore commonly used tools for prompt engineering to aid with prompt engineering.

There are 3 modules in this course

Prompt engineering involves crafting inputs that guide generative AI models to produce accurate, relevant, and high-quality outputs. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will apply various prompt engineering approaches such as the interview pattern, chain-of- thought, and tree-of-thought, which aim at generating precise and relevant responses. You will also explore text-to-image prompting techniques to generate high-impact visuals using image-generation models. Additionally, you will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust. The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Generative AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts. By the end of the course, you will have a strong foundation in prompt engineering techniques and strategies to effectively interact with generative AI systems.

Prompt Engineering: Techniques and Approaches

Course Quiz, Project, and Wrap-up