Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with 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 generation tools
- Identifying where prompt engineering delivers tangible 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
- Understanding the capabilities and limitations of pre-trained models
- How model architecture influences the strategy for writing prompts
Comparing the Leading AI Assistants
- Microsoft Copilot: Leveraging its strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding, while acknowledging limitations in creative range and deep reasoning compared to competitors.
- Google Gemini: Utilizing its native multimodality, Workspace integration, and real-time search capabilities, while noting challenges with inconsistency, regional availability, and instruction-following on complex tasks.
- ChatGPT: Capitalizing on its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while considering its reliance on grounding for factual accuracy and stricter premium feature limits.
- Claude: Exploiting its proficiency in long-context processing, nuanced reasoning, long-form writing, and analytical clarity, while noting its narrower tool ecosystem and limited image generation.
- Strategies for selecting the appropriate tool based on task requirements, audience, and compliance constraints.
- A comparative demonstration of running the same prompt across all four assistants.
Principles of Effective Prompt Design
- The three pillars of effective prompting: clarity, specificity, and context
- Structuring instructions, tone, format, and constraints
- Recognizing common pitfalls beginners encounter
- The iterative process of refining a weak prompt into a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three methods and identifying when to apply each
- Interpreting model behavior to adjust examples effectively
- Teaching a model new tasks using carefully selected few examples
- Hands-on exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts for nuanced results
- Applying style transfer, persona prompting, and creative direction
- 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 a model to niche tasks through example-driven prompts
- Determining when to use prompt engineering versus investing in fine-tuning
- Evaluating output quality and refining the approach iteratively
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence throughout multi-step generation processes
- Combining prompt patterns to achieve repeatable, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- An overview of 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 precisely control style, composition, lighting, and subject
- Using negative prompts, weighting, and iterative refinement
- Performing image-to-image transformation and editing via 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 materials, 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 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
- Privacy and data protection considerations for generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends over the next 12 months
- Course summary and recommended next steps
Requirements
Target Audience
Marketing, communications, and creative professionals exploring AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive tasks using prompt-driven tools. Beginners with no prior experience in AI or programming who seek a structured, tool-focused entry into the world 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)
