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 shaping the quality of AI output
- An overview of the current ecosystem of text, image, audio, and video tools
- Identifying where prompt engineering adds significant business value
Foundations of AI Models for Text and Image Generation
- A plain-language explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Understanding the strengths and limitations of pre-trained models
- Exploring why model architecture influences the way we craft prompts
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths in Microsoft 365 integration, Word, Excel, Outlook, and Teams workflows, plus enterprise data grounding; limitations in creative range and reasoning depth compared to competitors.
- Google Gemini: Strengths in native multimodality, Workspace integration, and real-time search grounding; challenges include inconsistency, regional availability, and handling complex instruction-following.
- ChatGPT: Strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; limitations involve factual reliability without grounding and stricter usage limits on premium features.
- Claude: Strengths in handling long contexts, nuanced reasoning, long-form writing, and clear-headed analysis; limitations include a narrower tool ecosystem and image generation capabilities.
- Guidance on choosing the appropriate tool for specific tasks, audiences, or compliance constraints.
- A side-by-side walkthrough comparing responses from all four assistants using the same prompt.
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three foundational pillars of a good prompt.
- Structuring instructions, tone, format, and constraints effectively.
- Recognizing common mistakes made by beginners and how to avoid them.
- Iterating from a weak prompt to a high-performing one.
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three approaches and knowing when to apply each one.
- Observing model behavior and adjusting examples accordingly.
- Teaching a model a new task using only a few carefully selected samples.
- Practical exercises across ChatGPT, Copilot, Gemini, and Claude.
Advanced Prompt Engineering Techniques
- Using 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.
- Techniques for reducing hallucinations, ambiguity, and bias in AI responses.
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and distinguishing it from full model training.
- Adapting a model to a niche task using example-driven prompts.
- Determining when prompt engineering is sufficient versus when fine-tuning is a better investment.
- 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 tasks.
- A brief look at customer support and chatbot use cases.
- 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.
- Crafting prompts that control style, composition, lighting, and subject matter.
- 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 the concepts of voice cloning and synthesis.
- Exploring use cases in training content, accessibility, and marketing.
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities.
- Scripting and storyboarding through prompt sequences.
- 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 text, image, audio, and video reasoning.
- Building end-to-end content pipelines without writing code.
- 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.
- Ensuring disclosure, transparency, and trust with end customers.
- Emerging tools, models, and trends to watch 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-assisted content production. It also suits business operations and customer-facing teams aiming to automate repetitive interactions through prompt-driven tools. Beginners with no prior experience in AI or programming who desire a structured, tool-focused entry into the world of generative AI will also benefit.
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)
