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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and how it distinguishes itself from traditional automation
- The impact of prompt engineering on the quality of AI outputs
- An overview of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers specific business value
Foundations of AI Models for Text and Image Generation
- A plain-language explanation of how large language models and diffusion models function
- Distinguishing between training data, fine-tuning, and prompting
- Recognizing the strengths and limitations of pre-trained models
- Understanding how model architecture influences prompt writing strategies
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 weaknesses in creative range and reasoning depth compared to competitors
- Google Gemini: showcasing its advantages in native multimodality, Workspace integration, and real-time search grounding, alongside weaknesses in consistency, regional availability, and complex instruction-following
- ChatGPT: emphasizing its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while acknowledging weaknesses in factual reliability without grounding and stricter limits on premium features
- Claude: focusing on its strengths in long-context handling, nuanced reasoning, long-form writing, and clear analysis, with limitations in tool ecosystem breadth and image generation capabilities
- Selecting the optimal tool based on specific tasks, target audiences, or compliance constraints
- A side-by-side demonstration of identical prompts across all four assistants
Principles of Effective Prompt Design
- Establishing clarity, specificity, and context as the core pillars of effective prompting
- Structuring instructions, tone, format, and constraints logically
- Identifying and recognizing common errors made by beginners
- The process of iterating from basic prompts to high-performing ones
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three approaches and determining when each is most appropriate
- Interpreting model behavior to adjust examples effectively
- Teaching a model new tasks using a small number of well-selected samples
- Practical exercises across ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using 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
- Mitigating hallucinations, ambiguity, and bias in AI responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full model training
- Adapting models to niche tasks through example-driven prompts
- Evaluating when prompt engineering is sufficient versus when fine-tuning offers better value
- Assessing output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence across multi-step generation processes
- Combining prompt patterns to achieve repeatable, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- An overview of customer support and chatbot applications
- Designing reusable prompt templates that do not require retraining
- Implementing quality control, escalation logic, and human-in-the-loop checkpoints
Image Generation and Manipulation
- Comparing DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts that control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations and edits through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text inputs
- Conceptual understanding of voice cloning and synthesis
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- An overview of current text-to-video tools and their realistic capabilities
- Scripting and storyboarding using prompt sequences
- Integrating AI-generated text, images, audio, and video into unified assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- How multimodal models unify reasoning across text, image, audio, and video
- Building end-to-end content pipelines without writing code
- Case studies from marketing, design, training, and advertising sectors
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to monitor over the next 12 months
Requirements
Targeted Audience
Marketing, communications, and creative professionals seeking to explore AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions using prompt-driven tools. Beginners with no prior experience in AI or programming who desire 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
