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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding the nature of generative AI and its distinction from traditional automation
- The impact of prompt engineering on the quality of AI-generated outputs
- A survey of the current landscape of text, image, audio, and video generation tools
- Identifying the business value added by proficient prompt engineering
Foundations of AI Models for Text and Image Generation
- Explaining the mechanics of large language models and diffusion models in accessible terms
- Distinguishing between training data, fine-tuning, and prompting
- Assessing the capabilities and limitations of pre-trained models
- Understanding how model architecture influences prompt construction
Comparing the Leading AI Assistants
- Microsoft Copilot: Highlighting its strengths in Microsoft 365 integration, workflows in Word, Excel, Outlook, and Teams, and enterprise data grounding, while noting its limitations in creative diversity and deep reasoning compared to competitors
- Google Gemini: Recognizing its advantages in native multimodality, Workspace integration, and real-time search grounding, alongside its challenges with consistency, regional availability, and complex instruction-following
- ChatGPT: Evaluating its mature ecosystem, custom GPTs, image generation via DALL-E, and voice capabilities, balanced against issues with factual reliability without grounding and stricter usage limits on premium features
- Claude: Appreciating its excellence in long-context handling, nuanced reasoning, and 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
- Conducting a comparative analysis by applying the same prompt across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core elements of effective prompts
- Organizing instructions, tone, format, and constraints for optimal results
- Identifying and correcting common errors made by beginners
- Refining low-quality prompts into high-performance ones through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between these three approaches and determining the appropriate context for each
- Interpreting model behavior and adjusting examples accordingly
- Instructing models on new tasks using a limited 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 to achieve nuanced outcomes
- Implementing style transfer, persona prompting, and creative direction
- Utilizing chain-of-thought and step-by-step reasoning to improve output logic
- Mitigating 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
- Tailoring models to specialized tasks through example-driven prompts
- Evaluating whether prompt engineering or fine-tuning offers greater value for specific needs
- Continuously assessing output quality and refining processes iteratively
Hyper-Realistic Text Generation
- Creating text with precise control over tone, voice, and length
- Generating long-form content, summaries, reports, and structured documents
- Ensuring coherence across multi-step generation processes
- Combining prompt patterns to achieve consistent, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Streamlining routine drafting, research, and information triage through automation
- Examining applications in customer support and chatbot interactions
- Creating 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
- Composing prompts to direct style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformations and edits using prompt-based methods
Audio and Speech with AI
- Synthesizing natural-sounding speech from textual prompts
- Understanding voice cloning and synthesis at a conceptual level
- Exploring applications in training materials, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing the current capabilities and realistic outputs of text-to-video tools
- Developing scripts and storyboards through sequential prompting
- Merging AI-generated text, images, audio, and video into cohesive assets
- Post-processing and refining AI-created video content
Multimodal AI and Integrated Workflows
- Understanding how multimodal models integrate reasoning across text, image, audio, and video
- Constructing end-to-end content pipelines without programming
- Analyzing real-world case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and What Comes Next
- 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 customers
- Monitoring emerging tools, models, and trends expected in the next 12 months
Requirements
Target Audience
This course is designed for marketing, communications, and creative professionals seeking to explore AI-assisted content creation. It also serves business operations and customer-facing teams aiming to automate repetitive interactions through prompt-driven solutions. It is particularly suitable for beginners with no prior experience in AI or programming who require a structured, tool-focused 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
