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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-generated output
- An overview of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering creates tangible business value
Foundations of AI Models for Text and Image Generation
- Explaining the mechanics of large language models and diffusion models in simple terms
- Distinguishing between training data, fine-tuning, and prompting
- Understanding the capabilities and limitations of pre-trained models
- Why model architecture influences prompt design strategies
Comparing Leading AI Assistants
- Microsoft Copilot: strong integration with Microsoft 365 (Word, Excel, Outlook, Teams) and enterprise data grounding, though limited in creative range and deep reasoning compared to competitors
- Google Gemini: excels in native multimodality, Workspace integration, and real-time search grounding, but may struggle with consistency, regional availability, and complex instruction-following
- ChatGPT: boasts a mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, yet faces challenges with factual reliability without grounding and stricter limits on premium features
- Claude: known for long-context handling, nuanced reasoning, and long-form writing, with weaknesses in the breadth of its tool ecosystem and image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration of the same prompt across all four assistants
Principles of Effective Prompt Design
- Mastering clarity, specificity, and context as the core elements of a successful prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying and correcting common beginner errors
- Iteratively improving a weak prompt to achieve high performance
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the differences between these approaches and when to apply each
- Interpreting model behavior and adjusting examples accordingly
- Training a model on new tasks using a small set of well-chosen samples
- Hands-on exercises using 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
- Implementing 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 ROI
- Evaluating output quality and refining through iteration
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 in multi-step generation processes
- Combining prompt patterns for consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- Exploring customer support and chatbot applications
- Designing 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
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts to control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text inputs
- Understanding voice cloning and synthesis at a conceptual level
- Applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Surveying current text-to-video tools and their realistic capabilities
- Creating scripts and storyboards using prompt sequences
- Synthesizing AI-generated text, images, audio, and video into cohesive assets
- Editing and refining AI-produced video content
Multimodal AI and Integrated Workflows
- Understanding 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
Ethics, Responsible Use, and Future Trends
- Navigating bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end-users
- Monitoring emerging tools, models, and trends for the next 12 months
Requirements
Target Audience
Professionals in marketing, communications, and creative roles seeking to enhance content production with AI. Teams in business operations and customer service aiming to streamline repetitive interactions through prompt-based tools. Additionally, beginners with no prior experience in AI or programming who desire a structured, tool-oriented introduction to 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
