Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI entails and how it contrasts with traditional automation
- The critical role of prompt engineering in determining the quality of AI 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 straightforward explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding how model architecture influences the way prompts are crafted
Comparing the Leading AI Assistants
- Microsoft Copilot: Known for seamless Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, though it may lag in creative range and deep reasoning compared to competitors
- Google Gemini: Excels in native multimodality, Workspace integration, and real-time search grounding, but can struggle with inconsistency, regional availability, and following complex instructions
- ChatGPT: Offers a mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, though it may face factual reliability issues without grounding and has stricter limits on premium features
- Claude: Stands out for handling long contexts, nuanced reasoning, long-form writing, and clear analysis, but has a narrower tool ecosystem and limited image generation capabilities
- Strategies for selecting the appropriate tool based on the task, audience, or compliance requirements
- A comparative walkthrough applying the same prompt across all four assistants
Principles of Effective Prompt Design
- The three core pillars of a strong prompt: clarity, specificity, and context
- Structuring instructions, tone, format, and constraints effectively
- Identifying common pitfalls beginners encounter and how to avoid them
- The process of iterating from an initial draft to a high-performing prompt
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the distinctions between these three approaches and when to apply each
- Observing model behavior and adjusting examples accordingly
- Instructing a model to perform a new task using only a few carefully selected examples
- 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
- Employing 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 specialized tasks through example-driven prompts
- Determining when to use prompt engineering versus when fine-tuning offers a better return on investment
- Evaluating output quality and refining the process iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over 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-consistent results
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 the need for 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 transformations and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech directly from text prompts
- Conceptual overview of voice cloning and synthesis
- Exploring use cases in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- An 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 cohesive asset
- Editing and refining video output created by AI
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
- 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
Professionals in marketing, communications, and creative fields who are interested in leveraging AI for content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior experience in AI or programming who seek 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)
