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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 survey of the current landscape of text, image, audio, and video tools
- How prompt engineering delivers tangible business value
Basics of AI Models for Text and Image Generation
- A simplified 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
- Why model architecture influences prompt construction
Evaluating Leading AI Assistants
- Microsoft Copilot: Highlighting its strengths in Microsoft 365 integration, workflows for Word, Excel, Outlook, and Teams, and enterprise data grounding, while noting weaknesses in creative versatility and deep reasoning compared to competitors
- Google Gemini: Featuring its native multimodality, Workspace integration, and real-time search grounding, alongside challenges such as inconsistency, regional availability, and instruction-following on complex tasks
- ChatGPT: Recognized for its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, but with caveats regarding factual reliability without grounding and stricter limits on premium features
- Claude: Valued for long-context processing, nuanced reasoning, and extensive writing capabilities, though limited by a narrower tool ecosystem and image generation options
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative walkthrough of the same prompt across all four assistants
Core Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the three foundations of a strong prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner errors and how to detect them
- Refining weak prompts into high-performing ones through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three approaches and understanding when to apply each
- Analyzing model behavior to adjust examples appropriately
- Training a model on new tasks using a few well-selected samples
- Practical exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Strategies
- Creating conditional and context-aware prompts for nuanced results
- Employing 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 using example-driven prompts
- Determining when to use prompt engineering versus when fine-tuning is a better investment
- Evaluating output quality and refining results iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Creating long-form content, summaries, reports, and structured documents
- Maintaining coherence throughout multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Workflows
- Automating routine drafting, research, and information sorting
- Exploring applications in customer support and chatbots
- Designing reusable prompt templates for teams without requiring retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Editing
- Comparing the capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that dictate style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and edits through prompting
Audio and Speech Applications
- Generating natural-sounding speech from text prompts
- Understanding the concepts behind voice cloning and synthesis
- Applying AI audio in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Synthesizing AI-generated text, images, audio, and video into a single asset
- Editing and polishing AI-created video content
Multimodal AI and Unified Workflows
- How multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Examining real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Usage, and Future Trends
- Addressing bias, copyright, attribution, and content moderation issues
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end-users
- Monitoring emerging tools, models, and trends for the next 12 months
Requirements
Intended Audience
This course is tailored for marketing, communications, and creative professionals interested in AI-assisted content production. It also suits business operations and client-facing teams aiming to streamline repetitive interactions using prompt-driven tools. It is an ideal starting point for beginners with no prior experience in AI or programming who seek a structured, tool-focused 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
