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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
- A survey of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Core Concepts of AI Models for Text and Image Generation
- Explaining large language models and diffusion models in accessible terms
- Distinguishing between training data, fine-tuning, and prompting
- Understanding the capabilities and limitations of pre-trained models
- How model architecture influences prompt construction
Evaluating Leading AI Assistants
- Microsoft Copilot, highlighting its integration with Microsoft 365, workflows in Word, Excel, Outlook, and Teams, and enterprise data grounding, while noting limitations in creative breadth and reasoning depth relative to competitors
- Google Gemini, focusing on native multimodality, Workspace integration, and real-time search grounding, with caveats regarding consistency, regional access, and instruction adherence in complex scenarios
- ChatGPT, emphasizing its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while addressing challenges with factual reliability without grounding and restrictions on premium features
- Claude, showcasing its strength in long-context processing, nuanced reasoning, long-form writing, and analytical clarity, while acknowledging constraints in tool ecosystem diversity and image generation
- Selecting the most appropriate tool based on specific tasks, target audiences, or compliance requirements
- A comparative demonstration of identical prompts across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the foundational elements of a robust prompt
- Organizing instructions, tone, formatting, and constraints
- Identifying common beginner errors and how to detect them
- Refining prompts iteratively from a basic draft to a high-performance result
Zero-Shot, One-Shot, and Few-Shot Prompting
- Understanding the distinctions among these three approaches and determining their optimal use cases
- Interpreting model behavior and modifying examples accordingly
- Instructing models on new tasks using a limited set of carefully selected samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts to achieve 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 Coding
- Defining few-shot fine-tuning and contrasting it with full model training
- Adapting models to specialized tasks through example-driven prompting
- Determining when to prioritize prompt engineering over fine-tuning
- Assessing output quality and refining results through iterative processes
Hyper-Realistic Text Generation
- Creating text with precise control over tone, voice, and length
- Producing extensive content, including summaries, reports, and structured documents
- Ensuring coherence throughout multi-step generation processes
- Leveraging prompt patterns to achieve consistent, brand-aligned outcomes
Integrating Prompt Engineering into Business Workflows
- Automating standard drafting, research, and information categorization
- Examining customer support and chatbot applications
- Creating reusable prompt templates for teams without the need for retraining
- Implementing quality controls, escalation protocols, and human-in-the-loop verification
Image Generation and Manipulation
- Comparing the capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Writing prompts to manage style, composition, lighting, and subject matter
- Using negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits via prompts
Audio and Speech with AI
- Generating natural-sounding speech from textual prompts
- Conceptual overview of voice cloning and synthesis
- Practical applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Surveying current text-to-video tools and their realistic capabilities
- Developing scripts and storyboards through sequential prompting
- Synthesizing AI-generated text, images, audio, and video into unified assets
- Editing and refining AI-produced 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
- Real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Usage, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when utilizing generative platforms
- Maintaining disclosure, transparency, and trust with end-users
- Monitoring emerging tools, models, and trends over the next 12 months
Requirements
Intended Audience
Marketing, communications, and creative professionals seeking to explore AI-assisted content production. Business operations and client-facing teams aiming to streamline repetitive interactions via prompt-driven solutions. Beginners with no prior experience in AI or programming who desire a structured, tool-centric 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
