Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it differs from traditional automation methods
- The critical role of prompt engineering in determining the quality of AI outputs
- A comprehensive overview 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 function
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt creation strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Advantages include seamless integration with Microsoft 365, Word, Excel, Outlook, and Teams, as well as enterprise data grounding; limitations include narrower creative range and less deep reasoning compared to competitors
- Google Gemini: Strengths lie in native multimodality, Workspace integration, and real-time search grounding; drawbacks include potential inconsistencies, regional availability issues, and difficulties following complex instructions
- ChatGPT: Notable for its mature ecosystem, custom GPT capabilities, DALL-E image generation, and voice mode; challenges involve factual reliability without grounding and stricter limitations on premium features
- Claude: Excels in handling long contexts, nuanced reasoning, long-form writing, and clear analytical thinking; however, it has a narrower tool ecosystem and limited image generation capabilities
- Strategies for selecting the appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative walkthrough of how each of the four assistants responds to identical prompts
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three foundational pillars of a strong prompt
- Techniques for structuring instructions, tone, format, and constraints
- Common pitfalls for beginners and how to identify and correct them
- The process of iterating from an ineffective prompt to a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three methods and determining when to apply each
- Observing model behavior and adjusting examples accordingly
- Instructing a model on a new task using only a select few examples
- Practical exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced results
- Employing style transfer, persona prompting, and creative direction
- Implementing chain-of-thought and step-by-step reasoning prompts
- Mitigating 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 specialized tasks through example-driven prompting
- Determining when to use prompt engineering versus investing in fine-tuning
- 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
- Ensuring coherence across multi-step generation processes
- Combining prompt patterns to achieve repeatable, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage tasks
- An overview of use cases in customer support and chatbot applications
- 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 editing via prompts
Audio and Speech with AI
- Creating natural-sounding speech from text prompts
- The conceptual framework behind voice cloning and synthesis
- Application scenarios in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Integrating AI-generated text, images, audio, and video into a single asset
- Editing and refining video outputs created by AI
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 issues
- Privacy and data protection considerations when utilizing generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to monitor over the next 12 months
- Course summary and recommended next steps
Requirements
Target Audience
This course is designed for marketing, communications, and creative professionals seeking to leverage AI-assisted content production. It also caters to business operations and customer-facing teams aiming to automate repetitive interactions using prompt-based tools. Additionally, it is ideal for beginners with no prior experience in AI or programming who desire 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)
