Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it differs from traditional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A comprehensive overview of the current ecosystem encompassing text, image, audio, and video tools
- Identifying where prompt engineering delivers 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
- Distinctions between training data, fine-tuning, and prompting
- Recognising the strengths and limitations of pre-trained models
- How model architecture influences the way we craft prompts
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths include deep integration with Microsoft 365, Word, Excel, Outlook, and Teams workflows, along with enterprise data grounding; limitations involve narrower creative range and reasoning depth compared to competitors
- Google Gemini: Strengths lie in native multimodality, Workspace integration, and real-time search grounding; drawbacks include occasional inconsistency, limited regional availability, and challenges with instruction-following on complex tasks
- ChatGPT: Strengths feature ecosystem maturity, custom GPTs, image generation via DALL-E, and voice mode; weaknesses include factual reliability issues without grounding and stricter usage limits on premium features
- Claude: Strengths encompass long-context handling, nuanced reasoning, long-form writing capabilities, and clear-headed analysis; limitations include a narrower tool ecosystem and limited image generation capabilities
- Strategies for selecting the most appropriate tool based on task requirements, audience, or compliance constraints
- A comparative walkthrough of the same prompt executed across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three foundational pillars of a high-quality prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common mistakes made by beginners and learning how to spot 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 knowing when to apply each
- Observing model behaviour and adjusting examples accordingly
- Teaching a model a new task using only a carefully selected few samples
- Practical exercises conducted across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilising 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
- Minimising hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Understanding what few-shot fine-tuning is and how it differs from full model training
- Adapting a model to a niche task using example-driven prompts
- Knowing when to rely on prompt engineering versus when fine-tuning offers a 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 the need for 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 transformation and editing through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Concepts behind 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 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
- Privacy and data protection considerations when using generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to watch over the next 12 months
- Summary and Next Steps
Requirements
Intended Audience
This course is designed for marketing, communications, and creative professionals who are exploring AI-assisted content production. It also suits business operations and customer-facing teams aiming to automate repetitive tasks using prompt-driven tools. Additionally, it is ideal for beginners with no prior experience in AI or programming who seek a structured, tool-oriented entry point 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)
