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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding generative AI and its distinctions from traditional automation
- The impact of prompt engineering on the quality of AI output
- An overview of the current landscape of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Foundations of AI Models for Text and Image Generation
- Explaining how large language models and diffusion models function in accessible terms
- Distinguishing between training data, fine-tuning, and prompting
- Assessing the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt structure
Comparing Leading AI Assistants
- Microsoft Copilot, highlighting its strengths in Microsoft 365 integration across Word, Excel, Outlook, and Teams, enterprise data grounding, while noting weaknesses in creative range and reasoning depth relative to competitors
- Google Gemini, focusing on native multimodality, Workspace integration, and real-time search grounding, with noted weaknesses in consistency, regional availability, and handling complex instructions
- ChatGPT, emphasizing its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while acknowledging challenges with factual reliability without grounding and stricter usage limits on premium features
- Claude, noted for its ability to handle long contexts, nuanced reasoning, and clear-headed analysis in longer-form writing, despite limitations in tool ecosystem breadth and image generation
- Selecting the appropriate tool based on specific tasks, audience needs, or compliance requirements
- A side-by-side demonstration of identical prompts across all four assistants
Principles of Effective Prompt Design
- Emphasizing clarity, specificity, and context as the core pillars of effective prompting
- Structuring instructions, tone, format, and constraints effectively
- Recognizing common errors made by beginners
- Refining prompts iteratively from weak to high-performing versions
Zero-Shot, One-Shot, and Few-Shot Prompting
- Differentiating between these three approaches and determining when to apply each
- Interpreting model behaviour and adjusting examples accordingly
- Guiding a model to perform a new task using a few well-selected samples
- Practical exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Using conditional and context-aware prompts to achieve nuanced outputs
- Applying style transfer, persona prompting, and creative direction strategies
- 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 using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning is a better investment
- Evaluating output quality and refining results iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Producing long-form content, including summaries, reports, and structured documents
- Maintaining coherence throughout 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
- Exploring customer support and chatbot use cases
- Designing 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
- Comparing the capabilities of DALL-E, Stable Diffusion, MidJourney, and Leonardo AI
- Crafting prompts to control style, composition, lighting, and subject matter
- Utilizing negative prompts, weighting, and iterative refinement techniques
- Performing image-to-image transformation and editing via prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding voice cloning and synthesis at a conceptual level
- Applying use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic deliverables
- Scripting and storyboarding through sequential prompts
- Synthesizing AI-generated text, images, audio, and video into unified assets
- Editing and refining AI-created video outputs
Multimodal AI and Integrated Workflows
- Understanding how multimodal models integrate reasoning across text, image, audio, and video
- Building end-to-end content pipelines without coding
- Analyzing real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation issues
- Considering privacy and data protection when using generative platforms
- Maintaining disclosure, transparency, and trust with end customers
- Monitoring emerging tools, models, and trends over the next 12 months
Requirements
Intended Audience
Professionals in marketing, communications, and creative fields seeking to explore AI-assisted content production. Business operations and customer-facing teams aiming to automate repetitive interactions via prompt-driven tools. Beginners with no prior experience in AI or programming who are looking for a structured, tool-centric entry point into 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
