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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from traditional automation
- The critical role prompt engineering plays in enhancing AI output quality
- A comprehensive overview of the current ecosystem of text, image, audio, and video tools
- Identifying where prompt engineering delivers tangible business value
Foundations of AI Models for Text and Image Generation
- A plain-language explanation of how large language models and diffusion models function
- Clarifying the distinctions between training data, fine-tuning, and prompting
- Understanding the strengths and limitations of pre-trained models
- Explaining how model architecture influences prompt writing strategies
Comparing the Leading AI Assistants
- Microsoft Copilot: highlighting its strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams), enterprise data grounding, and noting its limitations in creative range and deep reasoning compared to competitors
- Google Gemini: emphasizing its native multimodality, Workspace integration, and real-time search grounding, while addressing weaknesses in consistency, regional availability, and handling complex instructions
- ChatGPT: showcasing its mature ecosystem, custom GPTs, DALL-E image generation, and voice mode, while acknowledging limitations in factual reliability without grounding and stricter usage caps on premium features
- Claude: highlighting its superior long-context handling, nuanced reasoning, and long-form writing capabilities, while noting gaps in tool ecosystem breadth and image generation
- Selecting the most appropriate tool based on specific tasks, audiences, or compliance requirements
- A comparative walkthrough demonstrating the same prompt 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 effectively
- Identifying common beginner errors and learning how to detect them
- Iteratively refining weak prompts into high-performing ones
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between the three approaches and determining the most suitable context for each
- Interpreting model behavior and adjusting examples accordingly
- Teaching a model new tasks using a few well-selected samples
- Hands-on exercises utilizing ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Creating conditional and context-aware prompts for nuanced outputs
- 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 using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning offers better value
- Evaluating output quality and refining results through iteration
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
- 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 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
- Performing image-to-image transformation and editing through prompts
Audio and Speech with AI
- Generating natural-sounding speech from text prompts
- Understanding voice cloning and synthesis at a conceptual level
- Exploring use cases in training content, accessibility, and marketing
Video Content Creation with Generative AI
- Reviewing current text-to-video tools and their realistic capabilities
- Scripting and storyboarding through prompt sequences
- Synthesizing AI-generated text, images, audio, and video into a single asset
- Editing and refining AI-created video output
Multimodal AI and Integrated Workflows
- Understanding how multimodal models unify reasoning across text, image, audio, and video
- Building end-to-end content pipelines without coding
- Examining real-world case studies from marketing, design, training, and advertising
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
- Monitoring emerging tools, models, and trends expected over the next 12 months
Requirements
Targeted Audience
Marketing, communications, and creative professionals seeking to explore AI-assisted content production. Business operations and client-facing teams aiming to streamline repetitive interactions through prompt-driven tools. 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
