Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and its distinction from traditional automation
- The critical role of prompt engineering in determining the quality of AI output
- An overview 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 plain-language explanation of how large language models and diffusion models operate
- Distinguishing between training data, fine-tuning, and prompting
- Understanding the strengths and limitations of pre-trained models
- Why model architecture influences the way prompts are constructed
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, real-time search grounding; weaknesses in inconsistency, regional availability, and instruction-following for complex tasks
- ChatGPT: strengths in ecosystem maturity, custom GPTs, image generation via DALL-E, voice mode; weaknesses in 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; weaknesses in the breadth of its tool ecosystem and 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
- Clarity, specificity, and context as the three foundational pillars of an effective prompt
- Structuring instructions, tone, format, and constraints
- Identifying common beginner mistakes and learning to recognize 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 to use each
- Interpreting model behavior and adjusting examples accordingly
- Training a model on a new task using a small set of carefully selected samples
- Practical exercises conducted across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced outputs
- Applying style transfer, persona prompting, and creative direction
- Employing chain-of-thought and step-by-step reasoning prompts
- Mitigating hallucinations, ambiguity, and bias in responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and distinguishing it from full model training
- Adapting a model to a specialized task using example-driven prompts
- Determining when to rely on prompt engineering versus when fine-tuning is a better investment
- Evaluating output quality and refining results iteratively
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
- 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
- Using 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
- Understanding voice cloning and synthesis at a conceptual level
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing 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
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when utilizing generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Highlighting emerging tools, models, and trends to watch 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 aiming to automate repetitive interactions via prompt-driven tools. Beginners with no prior AI or programming experience seeking a structured, tool-centric entry point into 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)
