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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it 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 generation tools
- Identifying where prompt engineering drives business value
Foundations of AI Models for Text and Image Generation
- Simplified explanations of how large language models and diffusion models function
- Differentiating between training data, fine-tuning, and prompting
- Understanding the strengths and limitations of pre-trained models
- How model architecture influences prompt formulation
Comparison of Leading AI Assistants
- Microsoft Copilot: Strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding; weaknesses in creative breadth and complex reasoning compared to competitors
- Google Gemini: Strengths in native multimodality, Workspace integration, and real-time search grounding; weaknesses in consistency, regional availability, and handling complex instructions
- ChatGPT: Strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode; weaknesses in factual reliability without grounding and stricter premium usage limits
- Claude: Strengths in long-context processing, nuanced reasoning, long-form writing, and analytical clarity; weaknesses in tool ecosystem breadth and image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative walkthrough of the same prompt across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the core elements of successful prompts
- Organizing instructions, tone, format, and constraints
- Identifying and avoiding common beginner errors
- Refining weak prompts into high-performing ones through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three approaches and their appropriate use cases
- Interpreting model behavior and adjusting examples accordingly
- Guiding a model through new tasks using a small number of well-selected samples
- Practical exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced results
- Applying style transfer, persona prompting, and creative direction
- Implementing chain-of-thought and step-by-step reasoning prompts
- Mitigating hallucinations, ambiguity, and bias in 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 is a better investment
- Assessing output quality and refining through iterative processes
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 tasks
- Combining prompt patterns for consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Streamlining routine drafting, research, and information triage
- An overview of customer support and chatbot applications
- Developing reusable prompt templates for teams without the need for retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparative analysis of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts to control style, composition, lighting, and subject matter
- Using 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
- Applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- 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 cohesive assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when utilizing generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends for the next 12 months
Requirements
Target Audience
Marketing, communications, and creative professionals seeking to leverage AI for content production. Business operations and customer-facing teams aiming to streamline repetitive interactions using prompt-based tools. Beginners with no prior experience in AI or programming who require a structured, tool-centric pathway 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