Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and how it differs from traditional automation
- The role of prompt engineering in determining the quality of AI outputs
- An overview of the current landscape for text, image, audio, and video tools
- Where prompt engineering delivers business value
Foundations of AI Models for Text and Image Generation
- Plain-language explanations of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- The strengths and limitations of pre-trained models
- How model architecture influences prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding; weaknesses in creative range and reasoning depth compared to competitors
- Google Gemini: strengths in native multimodality, Workspace integration, and real-time search grounding; weaknesses in inconsistency, regional availability, and instruction-following on complex tasks
- ChatGPT: strengths in ecosystem maturity, custom GPTs, image generation via DALL-E, and voice mode; weaknesses in factual reliability without grounding and stricter usage limits on premium features
- Claude: strengths in long-context handling, nuanced reasoning, long-form writing, and clear analysis; weaknesses in tool ecosystem breadth and image generation capabilities
- Selecting the appropriate tool for specific tasks, audiences, or compliance requirements
- A side-by-side comparison of the same prompt executed across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three pillars of a strong prompt
- Structuring instructions, tone, format, and constraints
- Common beginner mistakes and how to identify them
- Iterating from a weak prompt to a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these three approaches and when to use each
- Observing model behaviour and adjusting examples accordingly
- Teaching a model a new task using a small selection of well-chosen samples
- Practical exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Conditional and context-aware prompts for nuanced outputs
- Style transfer, persona prompting, and creative direction
- Chain-of-thought and step-by-step reasoning prompts
- Minimising hallucinations, ambiguity, and bias in responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and how it differs from full model training
- Adapting a model to a niche task using example-driven prompts
- Knowing when to use prompt engineering versus fine-tuning for better return on investment
- Evaluating output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with controlled 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
- An overview of customer support and chatbot use cases
- Designing reusable prompt templates for teams without needing retraining
- 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
- Using negative prompts, weighting, and iterative refinement
- Image-to-image transformation and editing through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Conceptual overview of voice cloning and synthesis
- Use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Overview of current text-to-video tools and 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 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
- Bias, copyright, attribution, and content moderation
- Privacy and data protection considerations when using generative platforms
- Disclosure, transparency, and building trust with end customers
- Emerging tools, models, and trends to watch over the next 12 months
- Summary and Next Steps
Requirements
Target Audience
Marketing, communications, and creative professionals looking to leverage 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 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)
