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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and how it contrasts with traditional automation
- The impact of prompt engineering on the quality of AI outputs
- An overview of the current landscape of text, image, audio, and video tools
- How prompt engineering delivers business value
Foundations of AI Models for Text and Image Generation
- Explaining how large language models and diffusion models function in simple terms
- Distinguishing between training data, fine-tuning, and prompting
- Identifying the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt creation
Comparing Leading AI Assistants
- Microsoft Copilot, highlighting its strengths in Microsoft 365 integration, workflows in Word, Excel, Outlook, and Teams, and enterprise data grounding, while noting limitations in creative range and reasoning depth compared to competitors
- Google Gemini, showcasing its strengths in native multimodality, Workspace integration, and real-time search grounding, while acknowledging weaknesses in consistency, regional availability, and instruction-following for complex tasks
- ChatGPT, emphasizing its mature ecosystem, custom GPTs, image generation via DALL-E, and voice mode, while noting drawbacks in factual reliability without grounding and stricter usage limits on premium features
- Claude, recognized for its long-context handling, nuanced reasoning, long-form writing, and clear analysis, with limitations in tool ecosystem breadth and image generation capabilities
- Selecting the appropriate tool for specific tasks, 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 components of a strong prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner errors and how to spot them
- Refining a weak prompt into a high-performing one through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating the three approaches and determining when each is most appropriate
- Interpreting model behavior and adjusting examples accordingly
- Training a model on new tasks using a small number of well-selected samples
- Practical exercises spanning ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Crafting conditional and context-aware prompts for nuanced results
- Applying style transfer, persona prompting, and creative direction
- Utilizing 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 distinguishing it from full model training
- Adapting models to niche tasks using example-driven prompts
- Determining when to use prompt engineering versus when fine-tuning offers better value
- Assessing 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
Applying Prompt Engineering to Business Workflows
- Streamlining routine drafting, research, and information triage
- Examining customer support and chatbot applications
- Creating reusable prompt templates for teams without requiring retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts that control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding voice cloning and synthesis at a conceptual level
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Surveying current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-created video content
Multimodal AI and Integrated Workflows
- Understanding how multimodal models unify text, image, audio, and video reasoning
- Constructing end-to-end content pipelines without coding
- Reviewing real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using 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 explore AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior AI or programming experience who desire 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
