AI Prompt Engineering training course

Module 1: Introduction to Prompt Engineering

Understand how prompts control and shape AI responses across different platforms.

• What is prompt engineering?

• How AI models interpret instructions (LLMs, code models, image models)

• Overview of major tools:

  • Text: ChatGPT, Claude, Gemini, Perplexity

  • Code: GitHub Copilot, Amazon CodeWhisperer, Codeium

  • Visual: DALL·E, Midjourney, Runway ML

• Case Study: Compare the same writing prompt across ChatGPT, Claude, and Gemini

Practice:
Write a general-purpose prompt and analyze how different models respond.

Module 2: Prompt Structures and Parameters

Learn to control prompt behavior through structure and platform-specific settings.

• Prompt structure: instruction + context + output format

• Understanding key variables: temperature, max tokens, top-p

• Differences in prompt formatting across platforms

• System vs. user messages and how they affect results

Practice:
Design prompts for different use cases: writing, coding, and visual generation.

Module 3: Advanced Prompting Techniques

Expand your toolkit with advanced structures for greater control, tone, and complexity.

• Instruction-following vs. open-ended reasoning

• Multi-turn prompts using assistant and user roles

• Using personas, tone control, and embedded goals

• Designing prompts with specific stylistic or professional outcomes

Practice:
Transform a generic request into specialized prompts for different audiences or tools.

Module 4: Prompt Workflows and Skill Chains

Move beyond one-shot prompts by designing multi-step prompt systems.

• Introduction to prompt chaining and skill-based workflows

• Designing prompts that simulate step-by-step thinking

• Modular prompts: building components for reuse

• Case Study: Prompt chain for content generation and design automation

Project:
Create a multi-step workflow using a combination of tools (e.g., ChatGPT + DALL·E + Copilot).

Module 5: Testing, Evaluating, and Refining Prompts

Learn to iterate, evaluate, and improve your prompt performance.

• Prompt testing methods across tools

• Debugging and correcting prompt logic

• Common design pitfalls and how to address them

• Predicting AI output and reducing unexpected behavior

Practice:
Test and improve a prompt using iterative edits and model comparisons.

Module 6: Real-World Applications of Prompt Engineering

Apply your skills in practical business, creative, educational, and technical contexts.

• Business: customer support, report writing, sales messaging

• Creative: story generation, visual content creation, branding

• Education: tutoring, language learning, quiz generation

• Data: opinion mining, sentiment analysis, structured output generation

Project:
Build a prompt-driven solution for a chosen domain or challenge.

Peer Review:
Share and critique prompts in a structured feedback format.

Module 7: Wrap-Up and Future Outlook

Consolidate your knowledge and prepare to apply prompt engineering independently.

• Recap of core principles, patterns, and prompt structures

• Recommended tools, APIs, and learning resources

• Future trends: prompt agents, voice interfaces, real-time multimodal tools

• Final reflections and open Q&A

Final Activity:
Compile a personal prompt library with tested prompts tailored to your needs.

Learning Format

Each module includes:

• Lectures and theory walkthroughs

• Live or recorded demonstrations using real tools

• Hands-on exercises and reflection

• Structured feedback and peer reviews

• Group discussions or online Q&A forums

Course Outcomes

Upon completion, learners will be able to:

• Craft clear, effective prompts across various AI platforms

• Adapt prompts to different formats, styles, and tools

• Build multi-step workflows for repeatable AI-powered tasks

• Evaluate and refine prompts for better accuracy and usefulness

• Confidently apply prompt engineering in work, education, and creative fields