This six-week course is designed to take you from a beginner to a proficient AI prompt engineer. It focuses on practical application and iterative refinement, equipping you with the skills to effectively communicate with large language models (LLMs) and achieve desired outputs
Six-Week AI Prompt Engineering Course: From Basics to Advanced Applications
Course Goal: To provide participants with a comprehensive understanding of AI prompt engineering principles and practical techniques, enabling them to effectively design, evaluate, and optimize prompts for various AI applications
Target Audience: Anyone interested in maximizing the utility of AI tools, including content creators, marketers, developers, researchers, and general users of LLMs No prior coding experience is strictly required, but a basic understanding of how AI works is beneficial
Week 1: Foundations of Prompt Engineering & LLM Basics
Module 1: Introduction to Generative AI and LLMs
● What is AI, Machine Learning, and Deep Learning? (Brief overview)
● Introduction to Generative AI: How it works and its potential.
● Understanding Large Language Models (LLMs): How they generate text, their capabilities and limitations
● Key concepts: Tokens, parameters, pre-training, fine-tuning.
● Ethical considerations in AI and prompt engineering
Module 2: The Art of Prompting - Getting Started
● What is a prompt? The importance of clear and concise instructions
● Basic prompt structure: Instruction, context, input data, output format
● Zero-shot prompting: Asking a question directly.
● Hands-on exercises: Crafting simple prompts for various tasks (e g , summarization, text generation, question answering) using a popular LLM (e.g., ChatGPT, Gemini).
Practical Application: Experiment with different phrasing for simple requests and observe variations in AI responses
Week 2: Crafting Effective Prompts & Basic Techniques
Module 3: Principles of Effective Prompt Design
● Clarity and Specificity: Avoiding ambiguity
● Conciseness: Getting straight to the point
● Using action verbs and specifying desired actions.
● Defining output length and format (e g , bulleted list, essay, JSON)
● Specifying the target audience and tone
Module 4: Foundational Prompting Techniques
● Few-shot prompting: Providing examples within the prompt to guide the AI
○ Demonstrating the desired style, format, or content.
● Persona prompting: Assigning a specific role or persona to the AI
○ Examples: "Act as a marketing expert," "You are a friendly customer service agent "
● Hands-on exercises: Applying few-shot and persona prompting to improve output quality
Practical Application: Create prompts for specific scenarios like generating product descriptions or drafting an email in a particular style, leveraging few-shot and persona techniques.
Week 3: Advanced Prompting Strategies
Module 5: Chain-of-Thought (CoT) Prompting
● Understanding CoT: Guiding the LLM through intermediate reasoning steps
● When and why to use CoT.
● Techniques for implementing CoT: Step-by-step instructions, "think aloud" approach
● Hands-on exercises: Solving complex problems using CoT prompting.
Module 6: Iterative Prompt Development & Evaluation
● The iterative process: Prompt, evaluate, refine.
● Frameworks for evaluating AI outputs (e g , accuracy, relevance, coherence, completeness)
● Debugging prompts: Identifying why a prompt isn't working as expected.
● Refining prompts through follow-up questions and rephrasing
Practical Application: Work on a small project that requires multiple iterations of prompting and refinement to achieve the desired outcome
Week 4: Specialized Prompting & Control
Module 7: Controlling Output Size and Format
● Techniques for manipulating output length (e g , "summarize in 3 sentences," "write a 500-word essay")
● Enforcing specific output formats (e.g., bullet points, numbered lists, markdown, JSON).
● Using delimiters and structured input
Module 8: Prompting for Creative & Specific Tasks
● Prompting for creative writing (stories, poems, scripts)
● Generating code snippets and explanations
● Prompting for data analysis and summarization from provided text.
● Introduction to multimodal prompting (briefly, if tools are available)
Practical Application: Explore generating creative content, or using LLMs to assist with a simple coding task or data interpretation
Week 5: Prompt Engineering for Applications & Tools
Module 9: Integrating LLMs with APIs (Conceptual)
● Understanding LLM APIs: How developers interact with models programmatically
● Concepts of API keys, requests, and responses
● Brief introduction to popular LLM APIs (e.g., OpenAI API, Google Gemini API).
● Note: This module will be more conceptual unless participants have programming experience for hands-on work
Module 10: Advanced Prompt Patterns & Use Cases
● Common prompt patterns: Interview Pattern, Analogy Pattern, Audience Persona Pattern, etc.
● Building basic "AI agents" through prompt chaining (sequential prompting)
● Case studies of successful prompt engineering in real-world applications (e.g., chatbots, content generation tools)
Practical Application: Design a simple multi-step prompt flow for a specific application (e g , generating marketing copy, then a social media post from that copy).
Week 6: Advanced Topics, Best Practices & Project
Module 11: Bias, Safety, and Responsible AI
● Understanding biases in LLMs and how prompts can mitigate or exacerbate them
● Techniques for ensuring safe and ethical AI outputs.
● Recognizing and handling "hallucinations" in LLMs
● Best practices for responsible prompt engineering.
Module 12: Future of Prompt Engineering & Final Project
● Emerging trends in prompt engineering (e.g., automated prompt optimization, prompt marketplaces)
● Continuous learning and staying updated in the rapidly evolving AI landscape
● Final Project: Participants will design and execute a prompt engineering project of their choice, demonstrating their learned skills This could involve:
○ Building a sophisticated content generation system
○ Developing a prompt series for a specific business challenge.
○ Creating a complex reasoning chain for an AI
Practical Application: Work on and present the final project, showcasing the skills acquired throughout the course
Course Resources:
● Access to popular LLMs (e.g., ChatGPT, Google Gemini, Claude).
● Online prompt engineering guides and communities
● Selected readings and articles on prompt engineering best practices
Assessment:
● Weekly hands-on exercises and assignments
● Active participation in discussions.
● Final prompt engineering project
This course aims to be highly interactive, with a strong emphasis on hands-on practice and collaborative learning Participants will gain not only theoretical knowledge but also practical experience in mastering the crucial skill of prompt engineering
This is designed using Gemini pro and is the property of Emile Joseph Fagerstrom IV.