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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and how it distinguishes itself from conventional automation
- The critical role of prompt engineering in determining the quality of AI outputs
- A survey of the current landscape of text, image, audio, and video generation tools
- How prompt engineering drives tangible business value
Foundations of AI Models for Text and Image Generation
- A plain-language explanation of how large language models and diffusion models operate
- Distinguishing between training data, fine-tuning, and prompt engineering
- Understanding the capabilities and limitations of pre-trained models
- How model architecture influences prompt structure and effectiveness
Comparing the Leading AI Assistants
- Microsoft Copilot: leveraging strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, while noting limitations in creative breadth and deep reasoning compared to competitors
- Google Gemini: capitalizing on native multimodality, Workspace integration, and real-time search grounding, while addressing challenges related to consistency, regional availability, and complex instruction adherence
- ChatGPT: utilizing a mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while managing constraints regarding factual accuracy without grounding and premium usage limits
- Claude: benefiting from superior long-context processing, nuanced reasoning, and high-quality long-form writing, while acknowledging gaps in tool ecosystem diversity and image generation
- Strategies for selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative walkthrough demonstrating the same prompt across all four major assistants
Principles of Effective Prompt Design
- The three pillars of high-quality prompting: clarity, specificity, and contextual relevance
- Structuring instructions, defining tone, format, and constraints
- Identifying and avoiding common pitfalls faced by beginners
- The process of iterating from a basic prompt to a highly effective one
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three methodologies and determining their appropriate applications
- Interpreting model behavior to refine examples
- Teaching models new tasks using carefully selected few-shot samples
- Hands-on exercises applying these techniques across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for nuanced results
- Applying style transfer, persona definition, and creative direction
- Implementing chain-of-thought and step-by-step reasoning strategies
- Minimizing hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full-scale model training
- Adapting models to specialized tasks using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better value
- Assessing output quality and applying iterative refinement
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Creating long-form content, summaries, reports, and structured documents
- Maintaining narrative coherence across multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine tasks such as drafting, research, and information triage
- Exploring customer support and chatbot implementation use cases
- Designing reusable prompt templates for team collaboration without retraining
- Implementing quality control, escalation logic, and human-in-the-loop oversight
Image Generation and Manipulation
- Comparing the capabilities 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 for precision
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from textual inputs
- Understanding voice cloning and synthesis concepts
- Applying AI audio to training materials, accessibility features, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Using prompt sequences for scripting and storyboarding
- Synthesizing AI-generated text, images, audio, and video into cohesive assets
- Refining and editing AI-created video content
Multimodal AI and Integrated Workflows
- How multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Reviewing real-world case studies in marketing, design, training, and advertising
Ethics, Responsible Use, and What Comes Next
- Addressing 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 over the next 12 months
Requirements
Targeted Audience
Professionals in marketing, communications, and creative fields seeking to enhance content production through AI assistance. Business operations and client-facing teams aiming to streamline repetitive interactions using prompt-driven tools. Beginners without prior AI or programming experience who are looking for 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
