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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it diverges from traditional automation methods
- 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
- Explaining the mechanics of large language models and diffusion models in accessible terms
- Distinguishing between training data, fine-tuning, and prompting
- Evaluating the capabilities and limitations of pre-trained models
- Understanding how model architecture influences prompt writing strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Strengths in Microsoft 365 integration, Word, Excel, Outlook, and Teams workflows, and enterprise data grounding; weaknesses in creative range and deep reasoning compared to competitors
- Google Gemini: Strengths in native multimodality, Workspace integration, and real-time search grounding; weaknesses in consistency, regional availability, and following complex instructions
- ChatGPT: Strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; weaknesses in factual reliability without grounding and stricter usage limits on premium features
- Claude: Strengths in long-context handling, nuanced reasoning, long-form writing, and clear analysis; weaknesses in the breadth of its tool ecosystem and image generation capabilities
- Strategies for selecting the most appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative walkthrough applying the same prompt across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three cornerstones of a high-quality prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying and avoiding common mistakes made by beginners
- The iterative process of refining a weak prompt into a high-performing one
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these three approaches and the scenarios where each is most effective
- Interpreting model behavior and adjusting examples accordingly
- Training a model on new tasks using only a few carefully selected samples
- Hands-on exercises spanning ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts to achieve nuanced results
- Applying style transfer, persona prompting, and creative direction
- Employing 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 niche tasks through example-driven prompting
- Determining when prompt engineering is sufficient versus when fine-tuning offers better ROI
- Methods for 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 across multi-step generation processes
- Combining prompt patterns to achieve repeatable, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage processes
- An overview of 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
- A comparison 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 edits via prompting
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- An conceptual look at voice cloning and synthesis
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding through sequential prompting
- Synthesizing AI-generated text, images, audio, and video into single assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Building 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 issues
- Considering privacy and data protection when utilizing generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Identifying emerging tools, models, and trends to watch in the next 12 months
Requirements
Intended Audience
This course is designed for marketing, communications, and creative professionals aiming to explore AI-assisted content production. It also caters to business operations and client-facing teams seeking to automate repetitive interactions using prompt-driven tools. Finally, it serves as an ideal structured, tool-centric entry point for beginners with no prior experience in AI or programming who wish to dive into the world of 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
