Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and distinguishing it from traditional automation methods
- The critical role of prompt engineering in enhancing the quality of AI-generated outputs
- A comprehensive overview of the current landscape of text, image, audio, and video AI tools
- Identifying where prompt engineering delivers the most 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
- Differentiating between training data, fine-tuning processes, and prompt engineering
- Recognizing the strengths and limitations of pre-trained models
- Understanding why model architecture influences effective prompting strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding; weaknesses in creative range and deep reasoning compared to competitors
- Google Gemini: Strengths in native multimodality, Workspace integration, and real-time search grounding; weaknesses in consistency, regional availability, and complex instruction following
- ChatGPT: Strengths in ecosystem maturity, custom GPT creation, DALL-E image generation, and voice mode; weaknesses in factual reliability without grounding and stricter usage limits on premium features
- Claude: Strengths in handling long contexts, nuanced reasoning, long-form writing, and clear analytical capabilities; weaknesses in broader tool ecosystem and image generation
- Strategies for selecting the appropriate tool based on specific tasks, audiences, or compliance requirements
- A side-by-side practical demonstration of the same prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the foundational pillars of effective prompting
- Structuring instructions, tone, format, and constraints for optimal results
- Identifying common pitfalls beginners face and learning how to recognize them
- Iterative improvement: transforming weak prompts into high-performance inputs
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between the three approaches and identifying when to apply each
- Observing model behavior and adjusting examples to guide outcomes
- Teaching a model new tasks using a small set of carefully selected examples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using 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
- Mitigating hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and contrasting it with full model training
- Adapting models to niche tasks through example-driven prompting
- Deciding when to use prompt engineering versus fine-tuning for better ROI
- Evaluating output quality and refining approaches 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-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research tasks, and information triage
- Exploring use cases for customer support and chatbot integration
- Designing reusable prompt templates for team adoption without 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
- Writing prompts that control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting techniques, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text inputs
- Understanding the concepts behind voice cloning and synthesis
- Exploring applications in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Crafting scripts and storyboards through sequential prompting
- 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
- Building end-to-end content pipelines without coding
- Analyzing 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
- Identifying emerging tools, models, and trends to monitor over the next 12 months
- Summary and Next Steps
Requirements
Targeted Audience
This course is designed for marketing, communications, and creative professionals looking to leverage AI for content production. It also suits business operations and customer-facing teams aiming to automate repetitive tasks using prompt-driven tools. Ideal for beginners with no prior experience in AI or programming, it offers 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)
