AI Prompt Engineering Training in Kraków, Poland

This intensive workshop provides participants with hands-on, practical knowledge of prompt engineering — the skill of crafting effective instructions to communicate with AI systems. The course explores prompt design principles, platform-specific behaviors, workflow automation, and real-world applications across text, code, and image-based AI tools.

Participants will gain confidence in using prompts to control AI output across multiple platforms such as ChatGPT, Claude, GitHub Copilot, and Midjourney. The course combines theory, live demonstrations, and applied labs to ensure skill development through experience.

Foundations & Core Prompting Techniques

🔹 Module 1: Introduction to Prompt Engineering

• What is Prompt Engineering?
• Role of prompts in AI systems (Text, Code, Image models)
• How different AI models interpret instructions (LLMs, code models, diffusion models)
• Overview of major tools:
• Text: ChatGPT, Claude, Gemini, Perplexity
• Code: GitHub Copilot, Amazon CodeWhisperer, Codeium
• Visual: DALL·E, Midjourney, Runway ML
   

🔹 Module 2: Prompt Structures and Parameters

• Basic prompt structure: instruction + context + output format
• Prompt formats: one-shot, few-shot, and zero-shot
• Parameters and how they affect output:
• Temperature, max tokens, top-p
• Prompt formatting differences across tools and platforms
• System vs. user messages and their effect on output

🔹 Module 3: Advanced Prompting Techniques

• Instruction-following vs. open-ended prompts
• Multi-turn prompts: simulating dialogue with assistant/user roles
• Using tone, voice, and embedded objectives
• Designing prompts with personas and professional styles (e.g., formal, humorous, expert tone)
• Embedding constraints or guidelines into prompts

Workflows, Testing & Real-World Applications

🔹 Module 4: Prompt Workflows and Skill Chains

• Introduction to prompt chaining and multi-step prompting
• Step-by-step simulation (Chain-of-Thought prompting)
• Modular prompt design for reusability
• Designing skill chains to replicate domain-specific processes

🔹 Module 5: Testing, Evaluating, and Refining Prompts

• Prompt testing methods across tools and use cases
• Techniques for prompt debugging and improvement
• Common design pitfalls and how to correct them
• Predicting and controlling AI outputs
• Using comparison frameworks and evaluation metrics

🔹 Module 6: Real-World Applications of Prompt Engineering

• Business: Customer support automation, report drafting, sales messaging
• Creative: Storytelling, image generation, branding and marketing content
• Education: Tutoring assistants, quiz creation, content explanation
• Data/Analytics: Opinion mining, structured data output, sentiment analysis

🔹 Module 7: Wrap-Up and Future Outlook

• Recap of prompt engineering strategies and frameworks
• Tools, APIs, and libraries for deeper integration
• Emerging trends: prompt agents, real-time multimodal interaction, voice interfaces
• Final reflections, resource sharing, Q&A

Hands-On Exercises

Case Study, Polls and Quizzes