Course Outline
Day 1
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with 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 significant 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 strategies.
- Recognizing the strengths and limitations of pre-trained models.
- Understanding why model architecture influences prompt formulation.
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths include deep integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding; weaknesses lie in creative range and reasoning depth compared to competitors.
- Google Gemini: Strengths feature native multimodality, Workspace integration, and real-time search grounding; weaknesses involve inconsistency, regional availability issues, and challenges in following instructions for complex tasks.
- ChatGPT: Strengths encompass ecosystem maturity, custom GPT capabilities, DALL-E image generation, and voice mode; weaknesses include factual reliability when ungrounded and stricter usage limits on premium features.
- Claude: Strengths involve superior long-context handling, nuanced reasoning, long-form writing, and analytical clarity; weaknesses are limited tool ecosystem breadth and lack of image generation.
- Strategies for selecting the appropriate tool based on task requirements, audience, and compliance constraints.
- A comparative walkthrough executing the same prompt across all four assistants.
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the three foundational pillars of effective prompting.
- Structuring instructions, tone, format, and constraints for optimal results.
- Identifying common pitfalls made by beginners and learning how to recognize them.
- The process of iterating from a basic prompt to a high-performing one.
Day 2
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three approaches and determining when to apply each one.
- Observing model behavior and adjusting examples accordingly.
- Teaching a model a new task using only a few carefully selected samples.
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude platforms.
Advanced Prompt Engineering Techniques
- Developing conditional and context-aware prompts for more nuanced outputs.
- Utilizing 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 niche tasks through example-driven prompting.
- Determining when to invest in prompt engineering versus fine-tuning.
- Evaluating output quality and refining through iterative processes.
Hyper-Realistic Text Generation
- Producing text with controlled tone, voice, and length parameters.
- 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.
- An overview of customer support and chatbot use cases.
- Designing reusable prompt templates for teams without the need for retraining.
- Establishing 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.
- Crafting prompts that effectively control style, composition, lighting, and subject matter.
- Utilizing negative prompts, weighting techniques, and iterative refinement.
- Performing image-to-image transformation and editing via prompts.
Audio and Speech with AI
- Generating natural-sounding speech from text-based prompts.
- Conceptual overview of voice cloning and synthesis technologies.
- Exploring use cases in training content, accessibility, and marketing.
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities.
- Scripting and storyboarding through sequential prompts.
- Integrating AI-generated text, images, audio, and video into a single cohesive asset.
- 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 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
Targeted Audience
Marketing, communications, and creative professionals seeking to leverage AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior experience in AI or programming who desire a structured, tool-focused 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)
