Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it differs from traditional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers the most business value
Foundations of AI Models for Text and Image Generation
- A clear explanation of how large language models and diffusion models operate
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding how model architecture influences prompt design
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding, with limitations in creative range and reasoning depth
- Google Gemini: Strengths in native multimodality, Workspace integration, and real-time search grounding, with challenges regarding inconsistency, regional availability, and handling complex instructions
- ChatGPT: Strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode, with caveats on factual reliability without grounding and stricter limits on premium features
- Claude: Strengths in long-context handling, nuanced reasoning, long-form writing, and analytical clarity, with fewer tools in its ecosystem and limited image generation capabilities
- Strategies for selecting the appropriate tool based on task requirements, audience, or compliance needs
- A comparative demonstration of the same prompt processed by all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as foundational elements of a strong prompt
- Structuring instructions, tone, format, and constraints effectively
- Recognizing common pitfalls beginners encounter
- Techniques for iterating from a basic prompt to a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the distinctions between these approaches and knowing when to apply each
- Interpreting model behavior and adjusting examples accordingly
- Training a model to perform new tasks using carefully selected samples
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced outputs
- Applying style transfer, persona prompting, and creative direction
- Implementing chain-of-thought and step-by-step reasoning prompts
- Minimizing hallucinations, ambiguity, and bias in responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and distinguishing it from full model training
- Adapting models to specialized tasks using example-driven prompts
- Deciding when to use prompt engineering versus fine-tuning for better investment outcomes
- Evaluating output quality and refining results iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence during 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 tasks
- Exploring use cases for customer support and chatbots
- Designing reusable prompt templates for team-wide adoption
- Establishing 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
- Crafting prompts that precisely control style, composition, lighting, and subjects
- Using negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Conceptual overview of voice cloning and synthesis
- Applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Surveying current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards through sequential prompts
- Integrating AI-generated text, images, audio, and video into single assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Watching for emerging tools, models, and trends over the next 12 months
- Summary and Next Steps
Requirements
Targeted Audience
This course is designed for marketing, communications, and creative professionals seeking to leverage AI for content production. It also benefits business operations and customer-facing teams aiming to automate routine interactions via prompt-driven tools. Beginners with no prior experience in AI or programming will find this structured, tool-focused entry point particularly valuable for entering the field of 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)
