Unlocking AI and LLMs: The Complete Prompt Engineering Bootcamp

Teacher Open Source on GMAX Learn
Categories AI
Students 6 (Registered)
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Nov

Artificial Intelligence is only as powerful as the prompts you give it.

Prompt Engineering is the skill of designing clear, structured, and effective instructions that help AI tools like ChatGPT, Gemini, Claude, and others deliver accurate, creative, and useful results. This course is designed to help you move from random prompting to intentional, high-impact prompting.

In this hands-on course, you’ll learn how to communicate with AI systems effectively, optimize prompts for different use cases, and unlock AI’s full potential for work, business, creativity, and problem-solving.

Whether you’re a student, professional, creator, developer, marketer, or entrepreneur, this course equips you with a future-proof skill that cuts across industries.

What You’ll Learn

The fundamentals of Prompt Engineering and how AI models interpret prompts
How to structure prompts for clarity, accuracy, and depth
Prompt frameworks for different tasks (writing, research, coding, analysis, creativity)
How to refine and optimize prompts for better outputs
Common prompt mistakes and how to avoid them
Advanced prompting techniques for complex problem-solving
Ethical and responsible use of AI tools

Who This Course Is For

Beginners curious about AI and how it works
Professionals looking to boost productivity with AI
Content creators, marketers, and writers
Developers and tech enthusiasts
Entrepreneurs and business owners
Anyone who wants to stay relevant in the AI age

No prior AI or technical experience is required.

Course Format

Self-paced video lessons
Practical examples and real-world use cases
Simple explanations with actionable insights

Certification

Participants will receive a certificate of completion after successfully finishing the course, which can be presented and verified anywhere.

Important Note (Open-Source Listing)

This is an open-source course, and all credits belong to the original creator. GMAX Learn curates and organizes it for educational purposes, we do not sell or claim ownership of the content.

Curriculum

21 Sections
203 Lessons
Lifetime

Expand all sectionsCollapse all sections

Section 0: Introduction
3
- 1.1
  001. Learn To Work With AI & LLMs
- 1.2
  002. Course Introduction
- 1.3
  Course Resources + Handbook
Section 1: Introduction to Prompt Engineering
8
- 2.1
  003. What is Prompt Engineering?
- 2.2
  004. Why is Prompt Engineering Even a Thing?
- 2.3
  005. Breaking GPT
- 2.4
  006. Applied Prompt Engineering
- 2.5
  007. Applied Prompt Engineering with NASA
- 2.6
  008. Why is Prompt Engineering Important to You?
- 2.7
  The Latest: Am I Wrong?
- 2.8
  Mandatory Homework: AlphaGo
Section 2: Choose Your LLM
10
- 3.1
  009. What I’m Using – Part 1
- 3.2
  010. What I’m Using – Part 2 (OpenAI Playground)
- 3.3
  Optional: Setting Up Your Playground
- 3.4
  011. Multi-Modality and Tools in LLMs
- 3.5
  Choose Your LLM!
- 3.6
  012. Getting Started with ChatGPT
- 3.7
  013. The Basics of ChatGPT
- 3.8
  014. ChatGPT App
- 3.9
  015. Optional: ChatGPT Plus
- 3.10
  Getting Started with Llama
Section 3: Guided Project - Build Your First Game (Snake Game)
7
- 4.1
  016. Project Introduction
- 4.2
  Setting Up Your Replit Account
- 4.3
  Two Important Points
- 4.4
  The First Try
- 4.5
  Building Our Snake Game – Part 1
- 4.6
  Building Our Snake Game – Part 2
- 4.7
  Exercise: Make It Prettier!
Section 4: How LLMs Work
15
- 5.1
  Introduction to LLMs
- 5.2
  Introduction to LLMs Part 2
- 5.3
  Tokens
- 5.4
  Word Guessing Machines?
- 5.5
  Thinking Like LLMs – Roll a Dice
- 5.6
  Inside LLMs
- 5.7
  The Transformer Model
- 5.8
  Exercise: Visualize the LLM Architecture
- 5.9
  The Training Process
- 5.10
  Base Model vs. Assistant Model
- 5.11
  Thinking Like LLMs – The Reversal Curse
- 5.12
  Exercise: The Reversal Curse
- 5.13
  Artificial General Intelligence (AGI)
- 5.14
  Exercise: Use ChatGPT to Read the Research
- 5.15
  The World of LLMs
Section 5: Challenge - Build Your Second Game (Tic Tac Toe with AI opponent)
1
- 6.1
  Challenge Instructions
Section 6: Our Prompting Framework
4
- 7.1
  Overview of Our Prompting Framework
- 7.2
  The Standard Prompt
- 7.3
  Prompt Library
- 7.4
  Exercise: Get Hyped to Learn!
Section 7: Prompting Fundamentals - The Setup
21
- 8.1
  The Setup
- 8.2
  The System Message – Part 1
- 8.3
  The System Message – Part 2
- 8.4
  Exercise: Strengthen Your System Message
- 8.5
  Claude 3’s System Message
- 8.6
  Context – What it Is
- 8.7
  Context – The Context Window
- 8.8
  Context – Lost in the Middle
- 8.9
  The Latest: Gemini 1.5’s Context Window
- 8.10
  Personas and Roles
- 8.11
  Personas – Tone, Style and Voice
- 8.12
  Exercise: Talk To Your Hero
- 8.13
  Exercise: Write a Movie Scene
- 8.14
  Custom Instructions
- 8.15
  The System Message for Custom Instructions
- 8.16
  Thinking Like LLMs – Can GPT Keep a Secret?
- 8.17
  Exercise: Get ChatGPT to Spill the Tea – Part 1
- 8.18
  Exercise: Get ChatGPT to Spill the Tea – Part 2
- 8.19
  Exercise: Get ChatGPT to Spill the Tea – Part 3
- 8.20
  Exercise: Test Techniques for Securing Secrets
- 8.21
  Case Study: Moderna’s Use of ChatGPT
Section 8: Prompting Fundamentals - The Instructions
16
- 9.1
  The User Message
- 9.2
  Be Clear and Specific
- 9.3
  Delimiters
- 9.4
  Exercise: Identify Delimiters
- 9.5
  X-Y Problem
- 9.6
  Exercise: Identify X
- 9.7
  Solution: Identify X
- 9.8
  In-Context Training
- 9.9
  Zero Shot Prompting
- 9.10
  One Shot and Few Shot Prompting
- 9.11
  Language Models Are Few-Shot Learners
- 9.12
  How Many Examples?
- 9.13
  Thinking Like LLMs – But Wait…
- 9.14
  Chain-of-Thought Prompting – Part 1
- 9.15
  Chain-of-Thought Prompting – Part 2
- 9.16
  Zero Shot CoT
Section 9: Guided Project - Create Your Own Career Coach
8
- 10.1
  Career Coach Ideation
- 10.2
  The Setup – Persona
- 10.3
  The Setup – Context and Commands
- 10.4
  The Instructions – Modes
- 10.5
  The Instructions – Chain of Thought
- 10.6
  The Instructions – Gamify
- 10.7
  Meet Your Career Coach
- 10.8
  Exercise: Customize Your Career Coach
Section 10: Prompting Fundamentals - The Output
5
- 11.1
  The Output
- 11.2
  Length
- 11.3
  Formats
- 11.4
  Exercise: Output an Excel File
- 11.5
  Exercise: Make a Flowchart
Section 11: The Dark Side of AI (Guardrails, Jailbreaks, Injections, Hallucinations + more)
19
- 12.1
  Introduction to Guardrails and Jailbreaking
- 12.2
  Jailbreak! (The DAN Prompt)
- 12.3
  Many Shot Jailbreaking
- 12.4
  Prompt Injections – Part 1
- 12.5
  Prompt Injections – Part 2
- 12.6
  Thinking Like LLMs – Multi-Modal Injection
- 12.7
  Leaking – Part 1 (Prompt Leaking)
- 12.8
  Leaking – Part 2 (Data Leaking)
- 12.9
  Exposure
- 12.10
  Poisoning
- 12.11
  Toxicity
- 12.12
  Hallucinations
- 12.13
  Thinking Like LLMs – Big vs Small
- 12.14
  Challenge: Conduct Your Own Mechanistic Interpretability Research on Hallucinations
- 12.15
  Challenge Instructions
- 12.16
  Leaderboard: Mechanistic Interpretability
- 12.17
  The Model Card
- 12.18
  Model Cards Deep Dive
- 12.19
  Exercise: Explore the Latest Model Card and Learn Something New!
Section 12: LLM Hyperparameters & The OpenAI Playground
6
- 13.1
  Introduction to Hyperparameters and the OpenAI Playground
- 13.2
  Note: Setting Up Your Playground
- 13.3
  Temperature
- 13.4
  Top P
- 13.5
  Frequency and Presence Penalties
- 13.6
  Stop Sequences
Section 14: Open Source Models
10
- 14.1
  What Are Open Source LLMs and Why Are They Important?
- 14.2
  Chatbot Arena Leaderboard
- 14.3
  Battle of the Intelligence Tests
- 14.4
  Exercise: Test The Models With Your Specific Use-Case
- 14.5
  Exercise: Create Your Own Test Prompt (Totem)
- 14.6
  The Latest – The Best Open Source Model
- 14.7
  Introduction to LMStudio
- 14.8
  Setting Up Your Own Model – Part 1
- 14.9
  Setting Up Your Own Model – Part 2
- 14.10
  Exercise: Explore the Guardrails
Section 15: Advanced Prompting Techniques
22
- 15.1
  Introduction
- 15.2
  Auto-Priming
- 15.3
  Chain of Density Prompting
- 15.4
  Prompt Variables
- 15.5
  Prompt Chaining
- 15.6
  Prompt Chaining – Programmatic Visualization
- 15.7
  Exercise: Prompt Chaining – Customer Support
- 15.8
  Thinking Like LLMs – Dark Magic
- 15.9
  XML Tags – There’s More Than Meets The Eye!
- 15.10
  Emotional Stimuli – Part 1
- 15.11
  Emotional Stimuli – Part 2
- 15.12
  Emotional Stimuli – Part 3 (But Why?)
- 15.13
  Self-Consistency
- 15.14
  ReAct Prompting
- 15.15
  ReAct + CoT-SC
- 15.16
  Applied Prompt Engineering with CRISPR – Part 1
- 15.17
  Applied Prompt Engineering with CRISPR – Part 2
- 15.18
  Tree of Thoughts – Part 1
- 15.19
  Tree of Thoughts – Part 2
- 15.20
  Tree of Thoughts – Part 3 (ToT via Code)
- 15.21
  Tree of Thoughts – Part 4 (ToT via Chaining)
- 15.22
  Tree of Thoughts – Part 5 (“Zero Shot” ToT)
Section 16: Challenge - Build Your Third Game (Flappy Bird)
1
- 16.1
  Challenge Instructions
Section 17: Prompt Testing and Model Benchmarks
15
- 17.1
  Introduction to Prompt Testing
- 17.2
  The Importance of Prompt Testing
- 17.3
  Thinking Like LLMs – Schrodinger’s Prompt
- 17.4
  Building a Prompt Test
- 17.5
  The Golden Answer
- 17.6
  Model Benchmarks
- 17.7
  Deep Dive: MMLU Benchmark
- 17.8
  Prompt Tests vs. Model Benchmarks?
- 17.9
  The Latest: MMLU Pro
- 17.10
  Evaluating Results – Human Judge
- 17.11
  Evaluating Results – Code Judge
- 17.12
  Evaluating Results – AI Judge
- 17.13
  Deep Dive: LLMs as a Judge – Biases
- 17.14
  Deep Dive: LLMs as a Judge – Prompts
- 17.15
  Thinking Like LLMs – Ex Post Facto Reasoning
Section 18: Applied Prompt Engineering - Developing Prompt Tests & Evaluations with PromptFoo
14
- 18.1
  Here. We. Go!
- 18.2
  Introduction to PromptFoo
- 18.3
  Our Prompt Testing Framework
- 18.4
  Our 1st Prompt Test (Adding Prompts, Metrics + Human Judge) – The Setup
- 18.5
  Our 1st Prompt Test (Adding Prompts, Metrics + Human Judge) – The Results
- 18.6
  Our 2nd Prompt Test (Adding New Prompts + Code Judge) – The Setup
- 18.7
  Our 2nd Prompt Test (Adding New Prompts + Code Judge) – The Results
- 18.8
  Our 3rd Prompt Test (Adding 152 Test Cases + AI Judge) – The Setup
- 18.9
  Our 3rd Prompt Test (Adding 152 Test Cases + AI Judge) – The Results
- 18.10
  Our 4th Prompt Test (Adding Multiple Models) – The Setup
- 18.11
  Our 4th Prompt Test (Adding Multiple Models) – The Results
- 18.12
  Our 5th Prompt Test (Adding System Messages) – The Setup
- 18.13
  Our 5th Prompt Test (Adding System Messages) – The Results
- 18.14
  Do You Realize What Just Happened?
Section 19: AI Research and the Quest for Artificial General Intelligence
6
- 19.1
  Introduction – Through The Looking Glass…
- 19.2
  Applied Prompt Engineering with The Turing Test
- 19.3
  Mechanistic Interpretability – Part 1
- 19.4
  Mechanistic Interpretability – Part 2
- 19.5
  Scaling Laws – Model Size
- 19.6
  Scaling Laws – Dataset Size
Where To Go From Here?
1
- 20.1
  What’s Next?
Optional Appendix: Guided Project - Prompting with Autonomous Agents (AutoGPT)
11
- 21.1
  Project Introduction
- 21.2
  Setup Instructions
- 21.3
  Setup Demo: Getting Your API Key
- 21.4
  Setup Demo: Downloading AutoGPT
- 21.5
  Setup Demo: Installing Docker
- 21.6
  Launching Your Autonomous Agent – Part 1
- 21.7
  Launching Your Autonomous Agent – Part 2
- 21.8
  It’s Aliiiiiive! Running Your Autonomous Agent
- 21.9
  Task 1: Hello World – Your First Website
- 21.10
  Task 2: Python Program – Palindrome Checker
- 21.11
  Task 3: Initialize Your Own Task

Instructor

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