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Duration 21 hours
Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and its distinction from traditional automation
- The impact of prompt engineering on the quality of AI outputs
- An 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 plain-language explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- The strengths and limitations of pre-trained models
- How model architecture influences prompt construction
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 relative to competitors
- Google Gemini: strengths in native multimodality, Workspace integration, and real-time search grounding; weaknesses in consistency, regional availability, and complex instruction-following
- ChatGPT: strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; weaknesses in factual reliability without grounding and premium usage limits
- Claude: strengths in long-context handling, nuanced reasoning, long-form writing, and analytical clarity; weaknesses in tool ecosystem breadth and image generation capabilities
- Selecting the appropriate tool based on task, audience, or compliance requirements
- A comparative walkthrough applying the same prompt across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the core components of successful prompts
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner errors and recognizing them in practice
- Refining weak prompts into high-performance ones through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these approaches and identifying when each is most applicable
- Interpreting model behavior and adjusting examples accordingly
- Teaching models new tasks using a limited set of well-chosen samples
- Practical exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts to achieve nuanced outputs
- Applying style transfer, persona prompting, and creative direction strategies
- 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 its differences from full model training
- Adapting models to niche tasks via example-driven prompting
- Determining when prompt engineering suffices versus when fine-tuning offers better ROI
- 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 across multi-step generation processes
- Combining prompt patterns for consistent, 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 retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
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, and iterative refinement techniques
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Conceptual overview of voice cloning and synthesis
- Applications 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
- Integrating AI-generated text, images, audio, and video into unified assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Building end-to-end content pipelines without coding
- Real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Privacy and data protection considerations in generative platform usage
- Ensuring disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to monitor over the next 12 months
Requirements
Target Audience
Professionals in marketing, communications, and creative fields seeking AI-assisted content production. Business operations and customer-facing teams aiming to streamline repetitive interactions using prompt-driven tools. Additionally, it is designed for beginners with no prior AI or programming background who desire a structured, tool-centric introduction to generative AI.
Testimonials (2)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
use of proper and effective prompt
