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Course Outline
Foundations of Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with traditional automation
- The impact of prompt engineering on the quality of AI outputs
- An overview of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering drives business value
Basics of AI Models for Text and Image Creation
- Explaining the mechanics of large language models and diffusion models in simple terms
- Distinguishing between training data, fine-tuning, and prompting
- Examining the capabilities and limitations of pre-trained models
- Understanding how model architecture influences prompt writing strategies
Evaluating Leading AI Assistants
- Microsoft Copilot, highlighting its strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, while noting its limitations in creative breadth and deep reasoning compared to competitors
- Google Gemini, showcasing its native multimodality, Workspace integration, and real-time search grounding, alongside weaknesses in consistency, regional access, and handling complex instruction-following
- ChatGPT, recognized for its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, but constrained by factual reliability issues without grounding and stricter limits on premium features
- Claude, excelling in long-context processing, nuanced reasoning, long-form writing, and analytical clarity, yet limited by a smaller tool ecosystem and lack of native image generation
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative walkthrough testing the same prompt across all four platforms
Core Principles of Prompt Design
- Establishing clarity, specificity, and context as the key elements of effective prompts
- Organizing instructions, tone, format, and constraints
- Identifying common beginner errors and how to detect them
- Refining basic prompts into high-performing commands
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between these three approaches and determining when to use each
- Interpreting model behavior to adjust examples effectively
- Training a model on new tasks using a select few samples
- Hands-on exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Strategies
- Developing conditional and context-aware prompts for refined outputs
- Applying 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 Coding
- Defining few-shot fine-tuning and contrasting it with full model training
- Adapting models to specialized tasks using example-based prompts
- Deciding between prompt engineering and fine-tuning based on investment value
- Assessing output quality and refining results iteratively
High-Fidelity Text Generation
- Creating text with precise control over tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence throughout multi-step generation processes
- Using prompt patterns to achieve consistent, brand-aligned results
Integrating Prompt Engineering into Business Processes
- Automating routine drafting, research, and information sorting
- Briefly exploring customer support and chatbot applications
- Creating reusable prompt templates for teams without retraining
- Incorporating quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Editing
- Comparing capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts to direct style, composition, lighting, and subjects
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
AI for Audio and Speech
- Generating natural-sounding speech from text inputs
- Understanding voice cloning and synthesis at a conceptual level
- Applications in training materials, accessibility, and marketing
Creating Video Content with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Combining AI-generated text, images, audio, and video into unified assets
- Editing and polishing AI-created video content
Multimodal AI and Integrated Workflows
- Understanding how multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Examining real-world case studies in 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 users
- Monitoring emerging tools, models, and trends over the next year
Requirements
Intended Audience
This course is designed for marketing, communications, and creative specialists interested in AI-assisted content creation. It also suits business operations and client-facing teams aiming to streamline repetitive interactions using prompt-based tools. Additionally, it serves as a structured, tool-centric entry point for beginners with no prior background in AI or programming who wish to explore 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
