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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from conventional automation
- The impact of prompt engineering on the quality of AI-generated output
- An overview of the contemporary landscape of text, image, audio, and video generation tools
- Identifying the business value added by effective prompt engineering
Core Concepts of AI Models for Text and Image Creation
- Understanding the mechanics of large language models and diffusion models in simple terms
- Differentiating between training data, fine-tuning, and prompting
- Evaluating the capabilities and limitations of pre-trained models
- How model architecture influences prompt construction
Analysis of Leading AI Assistants
- Microsoft Copilot: Highlighting its strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, while noting its limitations in creative versatility and reasoning depth compared to competitors
- Google Gemini: Focusing on its native multimodality, Workspace integration, and real-time search capabilities, while acknowledging issues with consistency, regional access, and handling complex instructions
- ChatGPT: Emphasizing its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, alongside concerns regarding factual accuracy without grounding and usage restrictions on premium features
- Claude: Valuing its superior long-context handling, nuanced reasoning, long-form writing, and analytical clarity, while recognizing its more limited tool ecosystem and lack of built-in image generation
- Selecting the optimal tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration applying identical prompts across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the foundational elements of strong prompts
- Structuring instructions, tone, format, and constraints effectively
- Identifying and avoiding common errors made by beginners
- The process of refining weak prompts into high-performing ones
Zero-Shot, One-Shot, and Few-Shot Prompting Strategies
- Distinguishing between these three approaches and determining their appropriate use cases
- Interpreting model behavior and adjusting examples accordingly
- Instructing models on new tasks using a small number of carefully selected samples
- Hands-on practice exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Techniques in Prompt Engineering
- Using conditional and context-aware prompts to achieve nuanced results
- Applying style transfer, persona definition, and creative direction
- Implementing chain-of-thought and step-by-step reasoning prompts
- Mitigating hallucinations, ambiguity, and bias in AI responses
Code-Free Few-Shot Fine-Tuning
- Defining few-shot fine-tuning and how it contrasts with full model training
- Adapting models to specialized tasks using example-driven prompting
- Determining when prompt engineering is sufficient versus when fine-tuning offers greater ROI
- Assessing output quality and implementing iterative improvements
Generating Hyper-Realistic Text
- Creating text with precise control over tone, voice, and length
- Producing long-form articles, summaries, reports, and structured documents
- Maintaining logical coherence across multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Workflows
- Automating routine drafting, research, and information sorting
- Examining applications in customer support and chatbot deployments
- Creating reusable prompt templates for teams without the need for retraining
- Implementing quality control measures, escalation protocols, and human-in-the-loop verification
Image Generation and Manipulation
- Comparative analysis of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts that dictate style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Executing image-to-image transformations and edits via prompts
AI-Driven Audio and Speech Generation
- Synthesizing natural-sounding speech from textual inputs
- Understanding the concepts behind voice cloning and synthesis
- Exploring applications in training materials, accessibility features, and marketing campaigns
Creating Video Content with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards using sequential prompting
- Merging AI-generated text, images, audio, and video into cohesive assets
- Editing and polishing AI-generated video outputs
Multimodal AI and Integrated Workflows
- Understanding how multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without coding
- Studying real-world case studies from marketing, design, corporate training, and advertising
Ethics, Responsible Usage, and Future Trends
- Addressing issues of bias, copyright, attribution, and content moderation
- Considering privacy and data protection implications when using generative platforms
- Maintaining disclosure, transparency, and trust with end-users
- Identifying emerging tools, models, and trends to monitor over the next year
Requirements
Intended Audience
This course is ideal for marketing, communications, and creative professionals seeking to explore AI-assisted content creation. It also suits business operations and client-facing teams aiming to streamline repetitive interactions using prompt-driven solutions. Furthermore, it serves as a structured, tool-centric entry point for beginners who have no previous experience with AI or coding but wish to master 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
