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 broad overview 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
- A plain-language 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
- How model architecture influences prompt design strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: excels in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, but may lag in creative range and deep reasoning compared to competitors
- Google Gemini: offers native multimodality and Workspace integration with real-time search grounding, though it can struggle with inconsistency, regional availability, and complex instruction-following
- ChatGPT: boasts ecosystem maturity, custom GPT capabilities, DALL-E image generation, and voice mode, yet faces challenges with factual reliability without grounding and has stricter usage limits on premium features
- Claude: strong in handling long contexts, nuanced reasoning, long-form writing, and analytical clarity, but less broad in its tool ecosystem and image generation capabilities
- Guidance on selecting the appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative walkthrough applying the same prompt across all four major assistants
Principles of Effective Prompt Design
- The three core pillars of effective prompts: clarity, specificity, and context
- Structuring instructions, tone, format, and constraints effectively
- Common pitfalls for beginners and how to identify them
- Iterative refinement strategies to transform weak prompts into high-performing ones
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these three approaches and their ideal use cases
- Observing model behavior and adjusting examples accordingly
- Teaching a model a new task using a small set of carefully selected samples
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts for nuanced outputs
- Style transfer, persona prompting, and directing creative output
- Implementing chain-of-thought and step-by-step reasoning prompts
- Minimizing 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 models to niche tasks through example-driven prompts
- Deciding when to use prompt engineering versus when fine-tuning is a better investment
- Evaluating output quality and refining through iteration
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length
- Creating long-form content, summaries, reports, and structured documents
- Maintaining coherence throughout 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
- An overview of customer support and chatbot applications
- Designing reusable prompt templates for teams without retraining
- 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 control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Conceptual overview of voice cloning and synthesis
- Applications 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 using prompt sequences
- Integrating 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
- Privacy and data protection considerations when using generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to monitor over the next 12 months
- Summary and Next Steps
Requirements
Targeted Audience
Marketing, communications, and creative professionals interested in leveraging AI-assisted content production. Business operations and customer-facing teams seeking to automate repetitive tasks using prompt-driven tools. Beginners with no prior experience in AI or programming who desire a structured, tool-oriented introduction to 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)
