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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
- A landscape view of current text, image, audio, and video generation tools
- How prompt engineering drives tangible business value
Foundations of Text and Image Generation AI Models
- A plain-language explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- The capabilities and limitations of pre-trained models
- How model architecture influences prompt writing strategies
Evaluating Leading AI Assistants
- Microsoft Copilot: Highlights its deep integration with Microsoft 365 (Word, Excel, Outlook, Teams), enterprise data grounding, while noting its limitations in creative versatility and complex reasoning compared to competitors
- Google Gemini: Known for native multimodal capabilities, Workspace integration, and real-time search grounding, though it may face challenges with consistency, regional availability, and following complex instructions
- ChatGPT: Strong in ecosystem maturity, custom GPTs, DALL-E image generation, and voice features, yet constrained by factual reliability without external grounding and stricter limits on premium features
- Claude: Excels in long-context processing, nuanced reasoning, long-form writing, and analytical clarity, with fewer options for image generation and a narrower tool ecosystem
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative side-by-side demonstration of identical prompts across all four platforms
Principles of Effective Prompt Design
- Clarity, specificity, and context as the core components of effective prompts
- Organizing instructions, tone, format, and constraints
- Identifying common beginner errors and how to avoid them
- Refining low-performing prompts into high-quality ones through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the distinctions among these three approaches and when to apply each
- Interpreting model behavior to adjust examples effectively
- Training a model on new tasks using a small number of carefully selected samples
- Hands-on exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts for more nuanced results
- Techniques for style transfer, persona definition, and creative direction
- Employing 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
- Tailoring a model to specialized tasks via example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better ROI
- Assessing output quality and refining results iteratively
High-Fidelity Text Generation
- Generating text with precise control over tone, voice, and length
- Creating long-form articles, summaries, reports, and structured documents
- Maintaining coherence across multi-step generation tasks
- Applying prompt patterns for consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Workflows
- Automating routine drafting, research, and information sorting
- Exploring use cases in customer support and chatbots
- Creating reusable prompt templates for teams without needing retraining
- Implementing quality control, escalation logic, and human-in-the-loop oversight
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts that dictate style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and edits via prompting
Audio and Speech with AI
- Converting text prompts into natural-sounding speech
- Understanding voice cloning and synthesis concepts
- Applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Combining AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models integrate 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 Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considerations for privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end users
- Emerging tools, models, and trends to monitor over the next 12 months
Requirements
Intended Audience
Professionals in marketing, communications, and creative fields seeking AI-enhanced content production. Business operations and client-facing teams aiming to streamline repetitive interactions using prompt-based tools. Individuals new to AI with no prior programming background who desire a structured, tool-centric 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
