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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from traditional automation
- The impact of prompt engineering on the quality of AI outputs
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers business value
Foundations of AI Models for Text and Image Generation
- Explaining large language models and diffusion models in accessible terms
- Differentiating between training data, fine-tuning, and prompting
- Understanding the capabilities and limitations of pre-trained models
- How model architecture influences prompt writing strategies
Comparing the Leading AI Assistants
- Microsoft Copilot, highlighting its strengths in Microsoft 365 integration across Word, Excel, Outlook, and Teams, as well as enterprise data grounding, while noting its limitations in creative range and reasoning depth compared to competitors
- Google Gemini, emphasizing its native multimodality, Workspace integration, and real-time search grounding, while acknowledging issues with consistency, regional availability, and complex instruction-following
- ChatGPT, focusing on its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while noting weaknesses in ungrounded factual reliability and premium usage limits
- Claude, valued for its long-context handling, nuanced reasoning, and strong analytical writing, though it has a narrower tool ecosystem and limited image generation capabilities
- Selecting the appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative demonstration of identical prompts across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core pillars of effective prompting
- Organizing instructions, tone, format, and constraints
- Identifying and correcting common errors made by beginners
- The process of refining weak prompts into high-performing ones
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three approaches and determining when each is most applicable
- Interpreting model behavior to adjust examples effectively
- Teaching models new tasks using a small set of well-selected samples
- Hands-on exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Creating conditional and context-aware prompts for nuanced results
- 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 differentiating it from full model training
- Adapting models to specialized tasks using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better value
- Assessing output quality and refining it iteratively
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 across 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
- Briefly examining customer support and chatbot applications
- Developing reusable prompt templates for teams without requiring 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
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text inputs
- Understanding voice cloning and synthesis at a conceptual level
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Synthesizing AI-generated text, images, audio, and video into unified assets
- Editing and polishing AI-created video outputs
Multimodal AI and Integrated Workflows
- Understanding how multimodal models integrate text, image, audio, and video reasoning
- Constructing end-to-end content pipelines without coding
- Examining real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends for the next 12 months
Requirements
Targeted Audience
Professionals in marketing, communications, and creative fields who are exploring AI-assisted content production. Business operations and customer-facing teams seeking to streamline repetitive interactions using prompt-driven tools. Individuals new to the field with no prior AI or programming experience who desire a structured, tool-focused introduction to generative AI.
21 Hours
Testimonials (2)
use of proper and effective prompt
Marses Pacaldo
Course - Generative AI and Prompt Engineering for Corporate Professionals
The interactive style, the exercises
