Prompt Engineering Course
Welcome to our Prompt Engineering course — a modern guide to mastering communication with large language models like ChatGPT, Claude, and Gemini. Whether you’re a developer, educator, creator, or just AI-curious, this hands-on course will help you craft prompts that consistently yield accurate and powerful results.
By the end of this course, you’ll be able to build smart, prompt-driven workflows, debug responses, and use language models more effectively in real-world applications — from writing automation to AI-powered apps and data tools.
What You’ll Learn
- How large language models (LLMs) interpret and respond to prompts
- Basic and advanced prompt formats: instructional, zero-shot, few-shot
- Chain-of-thought reasoning, role-based prompts, and templates
- Common prompt anti-patterns and how to avoid them
- Prompt engineering for real-world tasks like summarization, generation, translation
- Integrating prompts into Python scripts, no-code tools, or AI apps
Who This Course Is For
- Software developers using OpenAI, Anthropic, or open-source LLMs
- Students, researchers, and tech enthusiasts exploring AI capabilities
- Content creators and marketers using AI for text generation
- Anyone who wants better results from tools like ChatGPT
Course Structure
| № | Chapter |
|---|---|
| 1 | Chapter 1: Introduction to Prompt EngineeringLearn the fundamentals of prompt engineering for AI models like GPT-4. Understand why prompting matters, its rise, and how it differs from traditional coding. |
| 2 | Understanding AI Model ArchitecturesExplore how AI architectures (Transformers, Diffusion models) influence prompt engineering. Learn context windows & tailor prompts for GPT, Claude, Midjourney. |
| 3 | Core Principles of Effective PromptingCore principles of effective prompting: clarity, precision, context, structure, examples (few-shot), formatting, and focus for better AI results. |
| 4 | Advanced Prompting TechniquesAdvanced prompt engineering techniques like Chain-of-Thought (CoT), RAG, Self-Consistency, System Messages, and Function Calling for complex tasks. |
| 5 | Domain-Specific PromptingLearn to tailor prompts for specific domains: generating text, writing code, creating images with tools like Midjourney, and performing data analysis. |
| 6 | Overcoming Common ChallengesAddress common LLM challenges: mitigating hallucinations, reducing bias, preventing prompt injection attacks, and handling model refusals effectively. |
| 7 | Prompt Engineering Case StudiesReal-world prompt engineering case studies: customer service chatbots, code assistants, image generation for brands, and data analysis helpers. |
| 8 | Evaluation and IterationHow to evaluate prompt performance using methods like golden sets, A/B testing, and LLM-as-a-judge. Master iterative prompt refinement strategies. |
| 9 | Future of Prompt EngineeringFuture of prompt engineering: multimodal prompting, automated optimization, prompt frameworks, intent specification, and the evolving role of tools. |