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
- An overview of the current landscape for text, image, audio, and video generation tools
- Identifying where prompt engineering delivers significant 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
- Recognizing the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt design strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Pros include robust integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding; cons involve limited creative range and reasoning depth compared to competitors
- Google Gemini: Pros feature native multimodality, Workspace integration, and real-time search grounding; cons include inconsistencies, regional availability issues, and difficulties with complex instruction-following
- ChatGPT: Pros encompass ecosystem maturity, custom GPT capabilities, DALL-E image generation, and voice mode; cons relate to factual reliability without external grounding and stricter usage limits on premium tiers
- Claude: Excels in handling long contexts, nuanced reasoning, long-form writing, and objective analysis; lacks breadth in tool ecosystems and dedicated image generation
- Strategies for selecting the appropriate tool based on task requirements, audience, or compliance needs
- A comparative walkthrough of a single prompt executed across all four assistants
Principles of Effective Prompt Design
- The three core pillars of effective prompting: clarity, specificity, and context
- Structuring instructions to define tone, format, and constraints
- Common beginner pitfalls and how to identify them
- The iterative process of transforming a weak prompt into a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three approaches and knowing when to apply each
- Observing model behavior to adjust examples effectively
- Teaching a new task to a model using a small set of carefully selected samples
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts for nuanced outputs
- Applying style transfer, persona prompting, and creative direction
- 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 to specialized tasks through example-driven prompting
- Determining when to rely on prompt engineering versus investing in actual fine-tuning
- Evaluating output quality and refining results iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Creating 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 tasks, and information triage
- Exploring use cases for customer support and chatbots
- 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 to control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting techniques, and iterative refinement
- Performing image-to-image transformations and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech directly from text prompts
- Understanding the concepts behind voice cloning and synthesis
- Application scenarios in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using sequential prompts
- Integrating AI-generated text, images, audio, and video into unified assets
- Editing and refining video outputs 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 coding knowledge
- 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 challenges
- 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 upcoming 12 months
- Course summary and recommended next steps
Requirements
Targeted Audience
This course is ideal for marketing, communications, and creative professionals seeking to leverage AI-assisted content production. It also suits business operations and customer-facing teams aiming to automate repetitive interactions via prompt-driven tools. Additionally, it serves as a structured, tool-focused entry point into generative AI for beginners with no prior experience in AI or programming.
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)
