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 shaping the quality of AI output
- A survey of the current ecosystem encompassing text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Foundations of AI Models for Text and Image Generation
- A plain-language explanation of how large language models and diffusion models operate
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding why model architecture influences the way we craft prompts
Comparing the Leading AI Assistants
- Microsoft Copilot: excels in Microsoft 365 integration (Word, Excel, Outlook, Teams workflows) and enterprise data grounding but may lag in creative range and reasoning depth compared to competitors
- Google Gemini: offers strengths in native multimodality, Workspace integration, and real-time search grounding, but can suffer from inconsistency, regional availability issues, and difficulties with complex instruction-following
- ChatGPT: boasts ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode, though it may face challenges with factual reliability without grounding and stricter usage limits on premium features
- Claude: known for handling long contexts, nuanced reasoning, long-form writing, and clear-headed analysis, but has a narrower tool ecosystem and limited image generation capabilities
- Strategies for selecting the right tool based on specific tasks, audiences, or compliance requirements
- A side-by-side walkthrough of the same prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the three pillars of a strong prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner mistakes and learning how to recognize 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 the three approaches and when to apply each
- Reading model behavior and adjusting examples accordingly
- Teaching a model a new task using only a few carefully selected samples
- Practical exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Crafting conditional and context-aware prompts for nuanced outputs
- Implementing style transfer, persona prompting, and creative direction
- Utilizing chain-of-thought and step-by-step reasoning prompts
- Reducing hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full model training
- Adapting a model to a niche task using example-driven prompts
- Determining when to rely on prompt engineering versus when fine-tuning offers better ROI
- 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
- Exploring customer support and chatbot use cases
- 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
- Writing prompts that effectively control style, composition, lighting, and subject matter
- Utilizing 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
- Conceptual overview of voice cloning and synthesis
- 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
- 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
- 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 watch in the next 12 months
- Summary and Next Steps
Requirements
Targeted Audience
Marketing, communications, and creative professionals keen on exploring AI-assisted content production. Business operations and customer-facing teams seeking to automate repetitive interactions using prompt-driven tools. Beginners with no prior experience in AI or programming 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)
