Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it diverges from traditional automation
- The critical role of prompt engineering in determining the quality of AI output
- A comprehensive overview of the current landscape for text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Foundations of AI Models for Text and Image Generation
- An accessible explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Recognising the strengths and limitations of pre-trained models
- Understanding how model architecture influences prompt design
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths include seamless Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding; limitations involve creative range and reasoning depth relative to peers
- Google Gemini: Strengths lie in native multimodality, Workspace integration, and real-time search grounding; limitations include inconsistency, regional availability, and adherence to complex instructions
- ChatGPT: Strengths encompass ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; limitations involve factual reliability without grounding and stricter premium usage limits
- Claude: Strengths feature long-context handling, nuanced reasoning, lengthy-form writing, and clear analytical capabilities; limitations include a narrower tool ecosystem and lack of image generation
- Selecting the appropriate tool based on task requirements, audience, or compliance constraints
- A side-by-side demonstration of a single prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the three pillars of effective prompting
- Structuring instructions, tone, format, and constraints
- Identifying common beginner mistakes 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
- Differentiating between the three approaches and determining when each is most suitable
- Interpreting model behaviour and adjusting examples accordingly
- Instructing a model on a new task using only a select 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
- Defining few-shot fine-tuning and distinguishing it from full model training
- Adapting a model for niche tasks through example-driven prompts
- Deciding when to rely on prompt engineering versus investing in fine-tuning
- 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
- Utilising negative prompts, weighting, and iterative refinement
- Performing image-to-image transformation and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Conceptual exploration of voice cloning and synthesis
- Use cases within 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 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
- Considering privacy and data protection when using generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Tracking emerging tools, models, and trends over the next 12 months
- Summary and Next Steps
Requirements
Targeted Audience
Marketing, communications, and creative professionals investigating AI-assisted content production. Business operations and customer-facing teams seeking to automate repetitive interactions via prompt-driven tools. Beginners with no prior AI or programming experience who desire a structured, tool-focused 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)
