Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it differs from traditional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A broad 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
- An accessible explanation of how large language models and diffusion models operate
- Distinguishing between training data, fine-tuning, and prompting
- Examining the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths include seamless integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding; limitations involve creative range and reasoning depth compared to competitors.
- Google Gemini: Strengths lie in native multimodality, Workspace integration, and real-time search grounding; limitations include occasional inconsistency, regional availability issues, and challenges with complex instruction adherence.
- ChatGPT: Strengths feature a mature ecosystem, custom GPTs, DALL-E image generation, and voice mode; weaknesses include potential factual inaccuracies without grounding and stricter limits on premium features.
- Claude: Strengths include handling long contexts, nuanced reasoning, long-form writing, and clear analytical insights; weaknesses are a narrower tool ecosystem and lack of built-in image generation.
- Strategies for selecting the appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative walkthrough of the same prompt across all four assistants
Principles of Effective Prompt Design
- The three foundational pillars of a strong prompt: clarity, specificity, and context
- Techniques for structuring instructions, tone, format, and constraints
- Common pitfalls encountered by beginners and methods to identify them
- The process of iterating from an initial, weak 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
- Teaching a model new tasks using carefully selected examples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced results
- 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 distinguishing it from full model training
- Adapting models to specialized tasks using example-driven prompts
- Determining when to rely on prompt engineering versus when fine-tuning offers better ROI
- Evaluating output quality and refining through iterative processes
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, and information triage tasks
- Exploring applications in customer support and chatbot scenarios
- Designing reusable prompt templates for team collaboration without retraining
- Implementing quality control measures, 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 effectively control style, composition, lighting, and subject matter
- Using negative prompts, weighting techniques, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text-based prompts
- Understanding voice cloning and synthesis at a conceptual level
- Exploring applications in training materials, accessibility solutions, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards through sequential prompting
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Techniques for editing and refining AI-generated video content
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Analyzing 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
- Considering privacy and data protection aspects when utilizing generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends over the coming 12 months
- Course summary and recommended next steps
Requirements
Target Audience
This course is designed for marketing, communications, and creative professionals interested in AI-assisted content creation. It is also suitable for business operations and customer-facing teams aiming to automate routine interactions using prompt-driven tools. Beginners with no prior experience in AI or programming who are seeking a structured, tool-centric introduction to generative AI will also find this training ideal.
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)
