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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 landscape overview of current text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Foundations of AI Models for Text and Image Generation
- Understanding 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
- How model architecture influences prompt construction strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: Excels in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, though it may lag in creative breadth and reasoning depth compared to competitors
- Google Gemini: Strong in native multimodality, Workspace integration, and real-time search grounding, despite challenges with consistency, regional availability, and complex instruction adherence
- ChatGPT: Benefits from a mature ecosystem, custom GPTs, DALL-E integration, and voice modes, but may require external grounding for factual reliability and has stricter limits on premium features
- Claude: Standout performance in long-context processing, nuanced reasoning, and long-form writing, although it has a narrower tool ecosystem and limited image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration using the same prompt across all four platforms
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core elements of effective prompting
- Organizing instructions, tone, format, and constraints
- Identifying and correcting common errors made by beginners
- Refining prompts iteratively to enhance performance
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between the three prompting approaches and determining their appropriate use cases
- Interpreting model behavior to adjust examples effectively
- Enabling new tasks using a small number of carefully selected samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using 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
- Minimizing hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Understanding few-shot fine-tuning and how it contrasts with full model training
- Adapting models to niche tasks through example-driven prompts
- Determining when to prioritize prompt engineering over fine-tuning
- Assessing output quality and refining results 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 throughout multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- Exploring 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
- Comparing capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts that direct 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
- Converting text prompts into natural-sounding speech
- Understanding voice cloning and synthesis concepts
- Identifying applications in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic output capabilities
- Developing scripts and storyboards through sequential prompting
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-produced video content
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Analyzing real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Considerations for privacy and data protection in generative platforms
- Maintaining disclosure, transparency, and trust with end users
- Monitoring emerging tools, models, and trends over the coming year
Requirements
Intended Audience
This course is tailored for marketing, communications, and creative professionals seeking to leverage AI for content production, as well as business operations and client-facing teams aiming to streamline repetitive interactions using prompt-based tools. It is particularly suitable for beginners with no prior experience in AI or programming who are looking for a structured, tool-oriented pathway 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
