Learn Prompting

Source: Learn Prompting Website

Introduction and Overview

The lessons provided by Learn Prompting serve as a community touchstone wildly cited by professionals in the prompt engineering community and adjacent technical areas involving generative AI and LLMs. The course aims to teach the process of communicating with AI effectively, making it a valuable skill in today's rapidly advancing AI technology. The course is comprehensive, covering topics from an introduction to AI to advanced PE techniques, and is designed for beginners. The creators of the course believe in making AI accessible to everyone, producing a comprehensive and unbiased course free of excessive jargon and hype.


This course was crafted with the following principles in mind: 1. Quick Iterations: course changes to reflect developments in the field and covers topics in concise articles covering the topic's breadth 2. Emphasis on Practicality: Focuses on applied, practical techniques of immediate utility to users accomplishing basic tasks with LLMs 3. Accessible Examples: to help students understand the material, clear and relevant examples are provided throughout 4. Collaborative Learning: authors are happy to help via GitHub issues and happy to take criticism on the material as well from the community.

How To Read

  • Material can be taken in any order
    • those new to the subject should probably start with the Basic lesson
  • Color coded article difficult rating system
    • green: beginner friendly
    • yellow: easy, requires basic programming knowledge
    • red: intermediate, requires programming and domain knowledge (aka related concepts from Math, easily explained by ChatGPT! ;])
    • purple:advanced, in-depth programming and domain knowledge


Each page of notes listed under this course under its hierarchy corresponds to one of the chapters of the course. Chapters are split into several individual articles and in some cases, may be split into multiple notes. To keep a logical paradigm of organization in maintaining these notes, each chapter will have its own directory and if subsequent notes are needed or desirable, they will correspond to an article within a chapter and be located in subdirectory of the chapter they are derived from. Below is a table enumerating each chapter and introducing its content in a very broad stroke.

1BasicsIntroduction to prompt engineering and fundamental techniques
2Basic Applications: Simple, practical applications of prompt engineering
3IntermediateResearch-based PE techniques with moderate complexity
4Applied PromptingComprehensive PE process walkthroughs contributed by community members
5Advanced ApplicationsPowerful, and more complex applications of prompt engineering
6ReliabilityEnhancing the reliability of large language models (LLMs)
7ImagesPrompt engineering for text-to-image models, such as DALL-E and Stable Diffusion
8Prompt InjectionHacking, but for prompt engineering
9ToolingA review of various prompt engineering tools and IDEs
10Prompt TuningRefining prompts using gradient-based techniques
11MiscellaneousA collection of additional topics and techniques related to prompt engineering