Learn Prompt Engineering for Generative AI

Learning prompt engineering from scattered blog posts and one-off tutorials can leave gaps in understanding, especially when it comes to connecting the theory of large language models to the practical skill of writing effective prompts. This video introduces an educational suite designed to close that gap, built as a structured set of slide decks, videos, and quizzes covering large language models, prompt engineering, and generative AI from the ground up.

Structure of the educational suite

The material is organized into three core areas: an introduction to large language models, an introduction to prompt engineering, and an introduction to generative AI. Each section is built as its own set of slide decks and videos, with the goal of letting learners move from historical context and foundational principles to hands-on prompt creation and real-world application.

Understanding large language models first

The first section covers the historical context behind large language models and their use cases, giving learners a grounding in how these systems developed before diving into how to use them effectively. This sequencing matters because prompt engineering makes more sense once you understand what the underlying model is actually doing when it responds to a prompt.

Prompt engineering fundamentals and patterns

The second section is where the course gets hands-on. It covers what a prompt actually is, how to create your first prompts, and the different types of prompt patterns available, including the persona pattern. Rather than treating prompt engineering as a single technique, the material breaks it down into distinct patterns that can be applied depending on the task at hand, giving learners a toolkit rather than a single recipe.

Applying the concepts with ChatGPT

To connect theory with practice, the suite integrates ChatGPT interactions with various prompt patterns, letting learners see how different approaches produce different results in a real chat interface. This use-cases section is meant to enrich the theoretical material with industry-relevant examples, showing how the same underlying model can be steered toward very different outputs depending on how it is prompted.

Generative AI fundamentals

The third section steps back to cover generative AI more broadly: its foundations and use cases before transformers existed, how text generation is evaluated, a deep dive into transformer architecture, how transformers generate text, and the overall life cycle of a generative AI system. This gives learners the technical context behind the tools they have just practiced using, tying the prompt engineering skills back to the architecture that makes them possible.

Key takeaways

• The course is organized into three parts: large language models, prompt engineering, and generative AI fundamentals.
• Prompt engineering is taught through distinct prompt patterns, including the persona pattern, rather than a single generic technique.
• Hands-on ChatGPT demonstrations connect the prompt patterns to real, observable differences in output.
• The generative AI section covers transformer architecture and the life cycle of text generation systems.
• All slide decks, videos, and reference material are available through the linked GitHub repository for self-paced learning.

Try it yourself

If you are new to prompt engineering, start with the large language models section to build context, then move through the prompt pattern examples with ChatGPT to see the differences firsthand. This material is part of Humanitarians AI's broader effort to make generative AI concepts accessible through structured, free educational resources.