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Course Outline
Introduction to Generative AI and Prompt Engineering
- Understanding what generative AI is and how it contrasts with traditional automation
- The pivotal role of prompt engineering in determining the quality of AI outputs
- 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 plain language
- Distinguishing between training data, fine-tuning, and prompting
- Assessing the strengths and limitations of pre-trained models
- Understanding why model architecture influences prompt creation
Comparing Leading AI Assistants
- Microsoft Copilot, highlighting its strengths in Microsoft 365 integration, Word, Excel, Outlook, and Teams workflows, as well as enterprise data grounding, while noting weaknesses in creative range and reasoning depth compared to competitors
- Google Gemini, featuring strengths in native multimodality, Workspace integration, and real-time search grounding, with noted weaknesses in consistency, regional availability, and instruction-following on complex tasks
- ChatGPT, praised for its mature ecosystem, custom GPTs, image generation via DALL-E, and voice mode, though it has weaknesses regarding factual reliability without grounding and stricter usage limits on premium features
- Claude, recognized for its long-context handling, nuanced reasoning, long-form writing, and analytical clarity, while having weaknesses in the breadth of its tool ecosystem and image generation capabilities
- Selecting the most suitable tool for specific tasks, audiences, or compliance requirements
- A comparative walkthrough of the same prompt applied across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the three core pillars of a high-quality prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common errors made by beginners and how to spot them
- Iterating from a basic prompt to a high-performing one
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between these three approaches and identifying when each is most appropriate
- Interpreting model behaviour and adjusting examples accordingly
- Teaching a model a new task using only a few well-selected samples
- Practical exercises across 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
- Implementing chain-of-thought and step-by-step reasoning prompts
- Minimising hallucinations, ambiguity, and bias in responses
Few-Shot Fine-Tuning Without Code
- Defining few-shot fine-tuning and differentiating it from full model training
- Adapting a model to niche tasks using example-driven prompts
- Determining when to prompt-engineer versus when fine-tuning offers better investment returns
- Evaluating output quality and refining through iterative processes
Hyper-Realistic Text Generation
- Generating text with controlled tone, voice, and length
- Producing long-form content, summaries, reports, and structured documents
- Maintaining coherence across multi-step generation
- Combining prompt patterns for repeatable, brand-aligned results
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- An overview of customer support and chatbot use cases
- Designing reusable prompt templates for teams without requiring 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
- Utilising negative prompts, weighting, and iterative refinement
- Performing image-to-image transformation and editing through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Voice cloning and synthesis at a conceptual level
- 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 through prompt sequences
- Combining AI-generated text, images, audio, and video into a single asset
- Editing and refining AI-created video output
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
- Real-world case studies from marketing, design, training, and advertising
Ethics, Responsible Use, and Future Trends
- Addressing bias, copyright, attribution, and content moderation
- Privacy and data protection considerations when using generative platforms
- Ensuring disclosure, transparency, and trust with end customers
- Emerging tools, models, and trends to monitor over the next 12 months
Requirements
Target Audience
Marketing, communications, and creative professionals investigating AI-assisted content production. Business operations and client-facing teams seeking to streamline repetitive interactions using prompt-driven tools. Beginners with no prior background in AI or programming who desire 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
