Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with traditional automation
- The pivotal role of prompt engineering in determining the quality of AI-generated outputs
- An overview of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible 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 formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: Highlighting its strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, alongside its limitations in creative range and reasoning depth compared to competitors.
- Google Gemini: Exploring its advantages in native multimodality, Workspace integration, and real-time search grounding, while noting challenges such as inconsistency, regional availability, and instruction-following on complex tasks.
- ChatGPT: Assessing its mature ecosystem, custom GPTs, DALL-E integration for image generation, and voice mode, alongside drawbacks like factual reliability issues without grounding and stricter usage limits on premium features.
- Claude: Evaluating its capabilities in handling long-context inputs, nuanced reasoning, long-form writing, and clear-headed analysis, while acknowledging limitations in tool ecosystem breadth and image generation.
- Strategies for selecting the appropriate tool based on specific tasks, audiences, or compliance requirements.
- A comparative walkthrough of the same prompt executed across all four assistants.
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the three foundational pillars of effective prompts.
- Structuring instructions, tone, format, and constraints.
- Identifying common mistakes made by beginners and learning how to spot them.
- Techniques for iterating from weak prompts to high-performing ones.
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three approaches and determining when each is most suitable.
- Observing model behavior and adjusting examples accordingly.
- Teaching a model new tasks using only a carefully selected few examples.
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude.
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced outputs.
- Employing 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 a model to niche tasks using example-driven prompts.
- Deciding 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 precise control over 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 integration.
- Designing reusable prompt templates for teams without requiring 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.
- Using negative prompts, weighting, and iterative refinement.
- Performing image-to-image transformations 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.
- Identifying 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 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 reasoning across text, image, audio, and video.
- Building end-to-end content pipelines without writing code.
- Real-world case studies from marketing, design, training, and advertising sectors.
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.
- Monitoring emerging tools, models, and trends over the next 12 months.
- Summary and Next Steps.
Requirements
Target 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 using prompt-driven tools. Additionally, it is ideal for beginners with no prior experience in AI or programming who desire a structured, tool-centric 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)
