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Course Outline
Introduction to Generative AI and Prompt Engineering
- What generative AI is and how it differs from traditional automation
- The role of prompt engineering in shaping AI output quality
- Overview of the current ecosystem of text, image, audio, and video tools
- Where prompt engineering adds business value
Foundations of AI Models for Text and Image Generation
- How large language models and diffusion models actually work, in plain terms
- The difference between training data, fine-tuning, and prompting
- Strengths and limits of pre-trained models
- Why model architecture changes the way we write prompts
Comparing the Leading AI Assistants
- Microsoft Copilot, with strengths in Microsoft 365 integration, Word, Excel, Outlook, and Teams workflows, enterprise data grounding, and weaknesses in creative range and reasoning depth compared to peers
- Google Gemini, with strengths in native multimodality, Workspace integration, real-time search grounding, and weaknesses in inconsistency, regional availability, and instruction-following on complex tasks
- ChatGPT, with strengths in ecosystem maturity, custom GPTs, image generation through DALL-E, voice mode, and weaknesses in factual reliability without grounding and stricter usage limits on premium features
- Claude, with strengths in long-context handling, nuanced reasoning, longer-form writing, and clear-headed analysis, with weaknesses in tool ecosystem breadth and image generation
- Choosing the right tool for a given task, audience, or compliance constraint
- A side-by-side walkthrough of the same prompt across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three pillars of a good prompt
- Structuring instructions, tone, format, and constraints
- Common mistakes beginners make and how to recognize them
- Iterating from a weak prompt to a high-performing one
Zero-Shot, One-Shot, and Few-Shot Prompting
- The difference between the three approaches and when each one fits
- Reading model behavior and adjusting examples accordingly
- Teaching a model a new task using only a few well-chosen samples
- Practical exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Conditional and context-aware prompts for nuanced outputs
- Style transfer, persona prompting, and creative direction
- Chain-of-thought and step-by-step reasoning prompts
- Reducing hallucinations, ambiguity, and bias in responses
Few-Shot Fine-Tuning Without Code
- What few-shot fine-tuning is and how it differs from full model training
- Adapting a model to a niche task using example-driven prompts
- When to prompt-engineer and when fine-tuning would be the better investment
- Evaluating output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence across multi-step generation
- Combining prompt patterns for repeatable, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- A short look at customer support and chatbot use cases
- Designing prompt templates teams can reuse without retraining
- Quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that control style, composition, lighting, and subject
- Negative prompts, weighting, and iterative refinement
- Image-to-image transformation and editing through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Voice cloning and synthesis at a conceptual level
- Use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Overview of current text-to-video tools and what they can realistically deliver
- Scripting and storyboarding through prompt sequences
- Combining AI-generated text, images, audio, and video into a single asset
- Editing and refining AI-created video output
Multimodal AI and Integrated Workflows
- How multimodal models unify text, image, audio, and video reasoning
- 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
- Bias, copyright, attribution, and content moderation
- Privacy and data protection considerations when using generative platforms
- Disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to watch over the next 12 months
Requirements
Targeted Audience
Marketing, communications, and creative professionals exploring AI-assisted content production. Business operations and customer-facing teams looking to automate repetitive interactions through prompt-driven tools. Beginners with no prior AI or programming experience who want a structured, tool-focused entry point into 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
