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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
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible 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
- Understanding the capabilities and limitations of pre-trained models
- How model architecture influences prompt strategy
Comparing the Leading AI Assistants
- Microsoft Copilot: Highlights its integration with Microsoft 365, Word, Excel, Outlook, and Teams workflows, as well as enterprise data grounding, while noting its limitations in creative range and reasoning depth compared to competitors
- Google Gemini: Focuses on its native multimodality, Workspace integration, and real-time search grounding, while addressing issues with consistency, regional availability, and complex instruction-following
- ChatGPT: Emphasizes its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while noting challenges with factual reliability without grounding and usage restrictions on premium features
- Claude: Known for long-context handling, nuanced reasoning, and strong long-form writing, while acknowledging its narrower tool ecosystem and lack of image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration of the same prompt executed 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 effectively
- Identifying common beginner errors and how to avoid them
- Refining a weak prompt into a high-performing one through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between these three approaches and determining when each is most appropriate
- Interpreting model behavior to adjust examples accordingly
- Training a model on new tasks using a small set of well-selected samples
- Hands-on practice across 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
- Minimizing 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 investing in fine-tuning
- Evaluating output quality and refining results iteratively
Hyper-Realistic Text Generation
- Creating text with precise control over tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence throughout multi-step generation processes
- Combining prompt patterns for consistent, brand-aligned outputs
Applying Prompt Engineering to Business Workflows
- Streamlining 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
- Evaluating DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that dictate style, composition, lighting, and subject matter
- Using 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 inputs
- Understanding voice cloning and synthesis concepts
- Applying AI in training content, accessibility features, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Structuring scripts and storyboards through sequential prompting
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-created video output
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Building end-to-end content pipelines without coding
- Case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and What Comes Next
- Addressing bias, copyright, attribution, and content moderation
- Considerations for 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
Intended Participants
Professionals in marketing, communications, and creative fields interested in AI-assisted content creation. Business operations and client-facing teams aiming to streamline repetitive interactions using prompt-based tools. Complete beginners with no prior experience in AI or programming who seek a structured, tool-centric 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
