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 enhancing AI output quality
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers significant 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
- Recognising the strengths and limitations of pre-trained models
- Understanding how model architecture influences prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: strengths in Microsoft 365 integration, Word, Excel, Outlook, and Teams workflows, plus enterprise data grounding; limitations in creative range and reasoning depth compared to competitors
- Google Gemini: strengths in native multimodality, Workspace integration, and real-time search grounding; limitations include inconsistency, regional availability, and difficulty with complex instruction following
- ChatGPT: strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; limitations involve factual reliability without grounding and stricter usage limits on premium features
- Claude: strengths in handling long contexts, nuanced reasoning, long-form writing, and clear analysis; limitations include a narrower tool ecosystem and limited image generation capabilities
- Strategies for selecting the appropriate tool based on task, audience, or compliance requirements
- A comparative walkthrough of the same prompt across all four assistants
Principles of Effective Prompt Design
- The three pillars of a strong prompt: clarity, specificity, and context
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner mistakes and learning to spot them
- Iterating from a basic prompt to a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these approaches and their optimal use cases
- Observing model behaviour and adjusting examples accordingly
- Training a model on a new task using only a few carefully selected examples
- Practical exercises using 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
- Defining few-shot fine-tuning and distinguishing it from full model training
- Adapting a model to specialised tasks using example-driven prompts
- Determining when to use prompt engineering versus when fine-tuning offers better value
- Evaluating output quality and refining through iteration
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length
- Producing 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, and information triage
- Exploring use cases for customer support and chatbots
- Designing reusable prompt templates for teams without requiring 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
- Using negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding voice cloning and synthesis at a conceptual level
- Exploring 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
- 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
- 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
- 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 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. Additionally, it is ideal for beginners with no previous 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)
