Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from traditional automation
- The critical role of prompt engineering in determining the quality of AI output
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers business value
Foundations of AI Models for Text and Image Generation
- Understanding how large language models and diffusion models function, explained simply
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Why model architecture influences the way we craft prompts
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths lie in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding; weaknesses include limited creative range and reasoning depth compared to competitors.
- Google Gemini: Strengths include native multimodality, Workspace integration, and real-time search grounding; weaknesses involve inconsistency, regional availability issues, and challenges following complex instructions.
- ChatGPT: Strengths feature ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; weaknesses involve factual reliability without grounding and stricter usage limits on premium features.
- Claude: Strengths include handling long-context data, nuanced reasoning, long-form writing, and clear analysis; weaknesses are a narrower tool ecosystem and limited image generation capabilities.
- Strategies for selecting the appropriate tool based on task requirements, audience, or compliance constraints.
- A side-by-side demonstration of the same prompt across all four assistants.
Principles of Effective Prompt Design
- The three pillars of a strong prompt: clarity, specificity, and context.
- Structuring instructions, tone, format, and constraints effectively.
- Identifying common mistakes made by beginners and learning to recognize them.
- Iterating from an initial weak prompt to a high-performing one.
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these approaches and knowing when to apply each.
- Observing model behavior and adjusting examples accordingly.
- Teaching a model a new task using only a few carefully selected samples.
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude.
Advanced Prompt Engineering Techniques
- Crafting conditional and context-aware prompts for nuanced outputs.
- Employing style transfer, persona prompting, and creative direction.
- Utilizing chain-of-thought and step-by-step reasoning prompts.
- Mitigating hallucinations, ambiguity, and bias in AI responses.
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full model training.
- Adapting a model to a specialized task using example-driven prompts.
- Deciding when to rely on prompt engineering versus investing in fine-tuning.
- Evaluating output quality and refining iteratively.
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length.
- Producing long-form content, summaries, reports, and structured documents.
- Maintaining coherence across multi-step generation processes.
- Combining prompt patterns to achieve repeatable, brand-aligned results.
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage.
- Exploring use cases in customer support and chatbot development.
- Designing reusable prompt templates for teams without requiring retraining.
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints.
Day 3
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI.
- Writing prompts that control style, composition, lighting, and subject.
- Utilizing negative prompts, weighting, and iterative refinement.
- Performing image-to-image transformation and editing through prompts.
Audio and Speech with AI
- Generating natural-sounding speech from text prompts.
- Understanding voice cloning and synthesis at a conceptual level.
- Exploring use cases in training content, accessibility, and marketing.
Video Content Creation with Generative AI
- Overview of current text-to-video tools and their realistic capabilities.
- Scripting and storyboarding through sequential prompts.
- Combining AI-generated text, images, audio, and video into a single asset.
- Editing and refining AI-created video output.
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video.
- Building end-to-end content pipelines without writing code.
- Reviewing real-world case studies from marketing, design, training, and advertising.
Ethics, Responsible Use, and Future Directions
- Addressing bias, copyright, attribution, and content moderation.
- Considering privacy and data protection when using generative platforms.
- Ensuring disclosure, transparency, and trust with end customers.
- Monitoring emerging tools, models, and trends over the next 12 months.
- Summary and Next Steps.
Requirements
Target Audience
Marketing, communications, and creative professionals investigating AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions via prompt-driven tools. Beginners with no prior experience in AI or programming who seek a structured, tool-focused entry point into generative AI.
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
