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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from conventional automation
- Understanding how prompt engineering influences the quality of AI outputs
- A survey 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
- A plain-language explanation of how large language models and diffusion models function
- Differentiating between training data, fine-tuning, and prompting
- Examining the capabilities and limitations of pre-trained models
- How model architecture influences prompt construction strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Highlights its strength in integrating with Microsoft 365 applications like Word, Excel, Outlook, and Teams, and its use of enterprise data grounding, while noting its limitations in creative breadth and deep reasoning compared to competitors
- Google Gemini: Noted for its native multimodal capabilities, Workspace integration, and real-time search grounding, though it faces challenges with consistency, regional availability, and handling complex instructions
- ChatGPT: Recognized for its mature ecosystem, custom GPTs, DALL-E integration, and voice mode, despite limitations in factual accuracy without grounding and stricter usage caps on premium features
- Claude: Praised for its long-context handling, nuanced reasoning, and strong performance in long-form writing and analytical tasks, although it has a narrower tool ecosystem and limited image generation
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A practical side-by-side comparison of the same prompt applied across all four assistants
Principles of Effective Prompt Design
- Mastering clarity, specificity, and context as the core elements of a high-quality prompt
- Organizing instructions, tone, format, and constraints effectively
- Identifying and correcting common pitfalls made by beginners
- Refining prompts iteratively to improve performance
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the distinctions between these three prompting approaches and their ideal applications
- Observing model behavior to tailor examples appropriately
- Enabling models to perform new tasks using carefully selected few-shot samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Crafting conditional and context-sensitive prompts for more nuanced results
- Employing style transfer, persona-based 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 contrasting it with full model training
- Adapting models to specialized tasks through example-driven prompting
- Determining when prompt engineering is sufficient versus when fine-tuning offers better value
- Assessing output quality and refining results iteratively
Hyper-Realistic Text Generation
- Controlling tone, voice, and length in generated text
- Creating long-form content, summaries, reports, and structured documents
- Maintaining logical coherence across multi-step generation processes
- Using prompt patterns to achieve consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Streamlining routine drafting, research, and information triage
- Exploring applications in customer support and chatbot interactions
- Developing reusable prompt templates for teams without the need for retraining
- Implementing quality controls, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- An evaluation of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that precisely control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Synthesizing natural-sounding speech from text prompts
- Conceptual understanding of voice cloning and speech synthesis
- Applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards using sequential prompts
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-generated video outputs
Multimodal AI and Integrated Workflows
- Understanding how multimodal models combine text, image, audio, and video reasoning
- Constructing end-to-end content pipelines without coding
- Analyzing real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and What Comes Next
- Navigating issues of bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Forecasting emerging tools, models, and trends for the coming year
Requirements
Target Audience
This course is ideal for marketing, communications, and creative professionals seeking to enhance their AI-assisted content production. It also serves business operations and customer-facing teams aiming to streamline repetitive interactions via prompt-based tools. Additionally, it provides a structured, tool-centric entry point for beginners with no 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
