Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with traditional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible 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
- Understanding the strengths and limitations of pre-trained models
- Why the underlying model architecture dictates prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths include deep integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding; weaknesses involve limited creative range and reasoning depth compared to competitors
- Google Gemini: Strengths lie in native multimodality, Workspace integration, and real-time search grounding; weaknesses include inconsistency, regional availability issues, and challenges with complex instruction following
- ChatGPT: Strengths feature a mature ecosystem, custom GPTs, DALL-E image generation, and voice mode; weaknesses involve factual reliability without grounding and stricter usage limits on premium features
- Claude: Strengths include superior long-context handling, nuanced reasoning, long-form writing capabilities, and clear-headed analysis; weaknesses involve a narrower tool ecosystem and less robust image generation
- Strategies for selecting the right tool based on task requirements, audience, or compliance constraints
- A comparative walkthrough of executing the same prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core pillars of a strong prompt
- Structuring instructions, tone, format, and constraints effectively
- Recognizing common pitfalls beginners encounter and how to identify them
- The process of 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 their ideal use cases
- Observing model behavior and adjusting examples to guide results
- Instructing a model to perform new tasks using only a few carefully selected samples
- Practical exercises conducted across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for more nuanced outputs
- Employing style transfer, persona prompting, and creative direction strategies
- Implementing chain-of-thought and step-by-step reasoning prompts
- Minimizing 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 using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better investment returns
- Techniques for evaluating output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over 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 tasks
- Exploring use cases in customer support and chatbot deployments
- Designing reusable prompt templates for team-wide adoption without 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 capabilities
- Crafting prompts that control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding the concepts behind voice cloning and synthesis
- Exploring use cases in training content, accessibility, and marketing campaigns
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding through sequences of prompts
- Integrating AI-generated text, images, audio, and video into a single asset
- Editing and refining video output created by AI
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without writing code
- Real-world case studies drawn from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation challenges
- Considering privacy and data protection implications when using generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends over the next 12 months
- Course summary and recommended next steps
Requirements
Target Audience
Marketing, communications, and creative professionals interested in AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior AI or programming experience seeking 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)
