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 output
- 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
- Exploring the capabilities and limitations of pre-trained models
- Understanding why model architecture influences prompt design
Comparing the Leading AI Assistants
- Microsoft Copilot: Benefits include deep integration with Microsoft 365, Word, Excel, Outlook, and Teams, as well as enterprise data grounding; limitations involve less creative range and reasoning depth compared to competitors
- Google Gemini: Advantages feature native multimodality, Workspace integration, and real-time search grounding; drawbacks include inconsistency, limited regional availability, and difficulties following complex instructions
- ChatGPT: Strengths lie in its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode; weaknesses include potential factual unreliability without grounding and stricter usage limits on premium tiers
- Claude: Excels in handling long contexts, nuanced reasoning, long-form writing, and clear analysis; limitations include a narrower tool ecosystem and weaker image generation capabilities
- Strategies for selecting the appropriate tool based on task requirements, audience, or compliance needs
- A comparative demonstration of how each assistant responds to the same prompt
Principles of Effective Prompt Design
- The three core pillars of effective prompts: clarity, specificity, and context
- Structuring instructions regarding tone, format, and constraints
- Recognizing and avoiding common pitfalls for beginners
- The process of refining a basic prompt into a high-performing one
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between these three approaches and identifying when to use each
- Observing model behavior and adjusting examples accordingly
- Training a model to perform new tasks using just a few well-selected examples
- Hands-on exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Crafting conditional and context-aware prompts for detailed outputs
- Utilizing 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 models for specialized tasks through example-driven prompting
- Deciding when to rely on prompt engineering versus when fine-tuning is more cost-effective
- Assessing output quality and iterating for improvement
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Creating long-form content, summaries, reports, and structured documents
- Ensuring coherence throughout multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- Exploring applications in customer support and chatbot development
- Designing reusable prompt templates for team-wide use without retraining
- Establishing quality control measures, escalation protocols, and human-in-the-loop checkpoints
Day 3
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that manage style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Producing natural-sounding speech from text prompts
- An overview of voice cloning and synthesis concepts
- Applications in training materials, accessibility services, and marketing
Video Content Creation with Generative AI
- A look at current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards using prompt sequences
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and refining video output 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 coding
- Real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright issues, attribution, and content moderation
- Considering privacy and data protection when utilizing generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Identifying emerging tools, models, and trends to monitor over the next year
- Course summary and future next steps
Requirements
Targeted Audience
This course is ideal for marketing, communications, and creative professionals interested in leveraging AI for content production. It also suits business operations and customer-facing teams aiming to automate repetitive tasks using prompt-driven tools. Additionally, it is designed for beginners with no prior experience in AI or programming who seek a structured, tool-oriented introduction to 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)
