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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from traditional automation
- Understanding how prompt engineering influences the quality of AI outputs
- A broad overview 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
- An accessible explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Examining the strengths and limitations of pre-trained models
- Why model architecture impacts prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: Strong integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding, though it may lag behind peers in creative range and reasoning depth
- Google Gemini: Excels in native multimodality, Workspace integration, and real-time search grounding, but may face issues with consistency, regional availability, and handling complex instructions
- ChatGPT: Offers a mature ecosystem, custom GPTs, image generation via DALL-E, and voice mode, yet has limitations in factual reliability without grounding and stricter premium feature limits
- Claude: Standout performance in long-context handling, nuanced reasoning, and long-form writing, though limited in tool ecosystem breadth and image generation capabilities
- Strategies for selecting the optimal tool based on specific tasks, audiences, or compliance needs
- A comparative walkthrough applying the same prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core components of effective prompts
- Structuring instructions, tone, format, and constraints
- Identifying common beginner errors and learning how to spot them
- The iterative process of transforming a weak prompt into a high-performing one
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these approaches and their ideal use cases
- Interpreting model behavior to adjust examples effectively
- Teaching models new tasks using a small number of well-selected samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Crafting 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
- Minimizing hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and contrasting it with full model training
- Adapting models to niche tasks through example-driven prompts
- Determining when to prompt-engineer versus when fine-tuning is a better investment
- Assessing output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining 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
- Exploring customer support and chatbot use cases
- Creating reusable prompt templates for teams without the need for retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparing capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that precisely control style, composition, lighting, and subject
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformation and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding voice cloning and synthesis at a conceptual level
- Applications 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 prompt sequences
- Integrating AI-generated text, images, audio, and video into unified assets
- Editing and refining AI-created video output
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
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Privacy and data protection considerations when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to watch over the next 12 months
Requirements
Target Audience
This course is designed for marketing, communications, and creative professionals exploring AI-assisted content production. It also suits business operations and customer-facing teams aiming to streamline repetitive interactions via prompt-driven tools. Ideally suited for beginners with no prior AI or coding background seeking 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
