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Course Outline
Basics of Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from conventional automation
- The impact of prompt engineering on the quality of AI-generated outputs
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Core Concepts of Text and Image AI Models
- An accessible explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Understanding the capabilities and constraints of pre-trained models
- How model architecture influences prompt formulation strategies
Evaluation of Major AI Assistants
- Microsoft Copilot: Highlighting its robust Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding, and noting limitations in creative versatility and deep reasoning relative to competitors
- Google Gemini: Focusing on its native multimodal capabilities, Workspace integration, and real-time search grounding, while acknowledging issues with consistency, regional access, and instruction-following in complex scenarios
- ChatGPT: Emphasizing its mature ecosystem, custom GPTs, image generation via DALL-E, and voice mode, while recognizing challenges in factual accuracy without grounding and stricter limits on premium features
- Claude: Recognizing its strength in handling long contexts, nuanced reasoning, extended writing, and analytical clarity, while noting its more limited tool ecosystem and lack of image generation
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration applying the same prompt across all four assistants
Essential Principles of Prompt Design
- Establishing clarity, specificity, and context as the foundational elements of effective prompting
- Structuring instructions, tone, format, and constraints effectively
- Identifying common errors made by beginners and how to detect them
- Progressing from basic prompts to high-performance instructions through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting Strategies
- Differentiating between these three approaches and determining when to apply each
- Analyzing model behavior and adjusting examples accordingly
- Guiding models toward new tasks using a small set of well-selected samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Methods
- Creating conditional and context-sensitive prompts for refined outputs
- Applying style transfer, persona-based prompting, and creative direction
- Implementing chain-of-thought and step-by-step reasoning techniques
- Minimizing hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Programming
- Defining few-shot fine-tuning and distinguishing it from comprehensive model training
- Adapting models to specialized tasks through example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better value
- Assessing output quality and refining results through iterative feedback
Creating Highly Realistic Text
- Generating text with precise control over tone, voice, and length
- Producing long-form articles, summaries, reports, and structured documents
- Ensuring coherence throughout multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Processes
- Streamlining routine drafting, research, and information triage
- Examining customer support and chatbot application scenarios
- Developing reusable prompt templates for teams without the need for retraining
- Implementing quality control, escalation logic, and human-in-the-loop oversight
Image Generation and Editing
- A comparison of 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 prompts
AI-Powered Audio and Speech
- Generating natural-sounding speech from text inputs
- Understanding voice cloning and synthesis at a conceptual level
- Exploring applications in training materials, accessibility, and marketing
Producing Video Content with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using sequential prompts
- Synthesizing AI-generated text, images, audio, and video into unified assets
- Editing and polishing AI-created video outputs
Multimodal AI and Unified Workflows
- Understanding how multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Reviewing real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Usage, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection implications of generative platforms
- Maintaining disclosure, transparency, and trust with end users
- Monitoring emerging tools, models, and trends for the coming year
Requirements
Intended Audience
Professionals in marketing, communications, and creative fields seeking to leverage AI for content production. Teams in business operations and customer service aiming to streamline repetitive interactions via prompt-based tools. Complete beginners with no background in AI or programming 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
