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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it diverges from conventional automation
- The critical role of prompt engineering in determining the quality of AI-generated output
- A broad look at the current landscape of text, image, audio, and video generation tools
- Identifying where prompt engineering creates tangible business value
Foundations of AI Models for Text and Image Generation
- Explaining the inner workings of large language models and diffusion models in accessible terms
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding why model architecture influences how prompts should be structured
Evaluating Leading AI Assistants
- Microsoft Copilot: Highlighting its strength in Microsoft 365 integration across Word, Excel, Outlook, and Teams, along with enterprise data grounding, while noting its comparative limitations in creative versatility and reasoning depth
- Google Gemini: Focusing on its native multimodal capabilities, Workspace integration, and real-time search grounding, while acknowledging challenges with consistency, regional availability, and instruction adherence in complex scenarios
- ChatGPT: Leveraging its mature ecosystem, custom GPTs, image generation via DALL-E, and voice mode, while being mindful of factual reliability issues without grounding and stricter limits on premium features
- Claude: Valuing its superior long-context handling, nuanced reasoning, and clarity in long-form writing and analysis, while recognizing gaps in its tool ecosystem and image generation capabilities
- Selecting the most appropriate tool based on specific tasks, target audiences, or compliance requirements
- Conducting a parallel walkthrough of the same prompt across all four assistants
Core Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the foundational pillars of successful prompting
- Organizing instructions, tone, format, and constraints effectively
- Identifying and correcting common errors made by beginners
- Iteratively refining weak prompts into high-performing ones
Zero-Shot, One-Shot, and Few-Shot Prompting Strategies
- Distinguishing between these three approaches and understanding when each is most appropriate
- Interpreting model behavior to adjust examples accordingly
- Instructing a model on new tasks using a small set of well-selected samples
- Engaging in practical 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
- 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 contrasting it with full model training
- Adapting a model to niche tasks using example-driven prompting
- Determining when prompt engineering is sufficient versus when fine-tuning offers better return on investment
- Assessing output quality and refining results through iterative processes
Generating Hyper-Realistic Text
- 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 tasks
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Workflows
- Automating routine drafting, research, and information triage processes
- Exploring use cases in customer support and chatbot deployment
- Developing reusable prompt templates for teams that eliminate the need for retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparing the capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts that dictate style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and editing through prompts
AI-Driven Audio and Speech Generation
- Synthesizing natural-sounding speech from text inputs
- Understanding voice cloning and synthesis concepts
- Exploring applications in training content, accessibility, and marketing
Creating Video Content with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards through sequential prompting
- Integrating AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-created video outputs
Multimodal AI and Integrated Workflows
- Understanding how multimodal models unify reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without writing code
- Analyzing real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation issues
- Considering privacy and data protection implications 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 Audience
Marketing, communications, and creative professionals seeking to adopt AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior background in AI or programming who desire 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
