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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and how it distinguishes itself from traditional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A survey of the current landscape of text, image, audio, and video generation 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
- Distinguishing between training data, fine-tuning, and prompting
- Exploring the capabilities and limitations of pre-trained models
- Why model architecture influences the way prompts are constructed
Comparing the Leading AI Assistants
- Microsoft Copilot: Highlighting strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, while noting limitations in creative range and reasoning depth compared to competitors
- Google Gemini: Focusing on native multimodality, Workspace integration, and real-time search grounding, alongside challenges with consistency, regional availability, and complex instruction-following
- ChatGPT: Emphasizing ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode, while acknowledging issues with factual reliability without grounding and stricter usage limits on premium features
- Claude: Valued for long-context handling, nuanced reasoning, and long-form writing, though limited by a narrower tool ecosystem and lack of image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A side-by-side demonstration of a single prompt executed across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context: the three pillars of effective prompting
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner errors and strategies to recognize them
- The iterative process of evolving a weak prompt into a high-performance one
Zero-Shot, One-Shot, and Few-Shot Prompting
- Defining the three approaches and determining when each is most appropriate
- Interpreting model behavior and adjusting examples accordingly
- Guiding a model through new tasks using a small set of well-selected samples
- Practical exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts to achieve nuanced outputs
- Applying style transfer, persona prompting, and creative direction
- Implementing chain-of-thought and step-by-step reasoning prompts
- Strategies for mitigating hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full model training
- Adapting models to niche tasks through example-driven prompts
- Decision-making frameworks: when to prompt-engineer 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 repeatable, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- Examining customer support and chatbot use cases
- Designing reusable prompt templates for teams without requiring 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
- Crafting prompts to control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text inputs
- Conceptual overview of voice cloning and synthesis
- Practical applications in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding through sequential prompts
- Synthesizing 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 integrate text, image, audio, and video reasoning
- Constructing end-to-end content pipelines without coding
- Real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Privacy and data protection considerations for generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Key emerging tools, models, and trends to monitor over the next 12 months
Requirements
Intended Audience
This course is designed for marketing, communications, and creative professionals seeking to explore AI-assisted content production. It also suits business operations and customer-facing teams aiming to streamline repetitive interactions using prompt-driven tools. Additionally, it serves as an ideal structured entry point for beginners with no prior experience in AI or programming who wish to focus on practical tool usage.
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
