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Course Outline
Introduction to Generative AI and Prompt Engineering
- Defining generative AI and distinguishing it from conventional automation
- The impact of prompt engineering on the quality of AI-generated 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
- Understanding the mechanics of large language models and diffusion models in simple terms
- Distinguishing between training data, fine-tuning, and prompting
- Assessing the capabilities and limitations of pre-trained models
- How model architecture influences prompt formulation
Comparing the Leading AI Assistants
- Microsoft Copilot: highlighting strengths in Microsoft 365 integration (Word, Excel, Outlook, Teams) and enterprise data grounding, while noting limitations in creative range and deep reasoning compared to competitors
- Google Gemini: showcasing strengths in native multimodality, Workspace integration, and real-time search grounding, while addressing weaknesses in consistency, regional availability, and handling complex instructions
- ChatGPT: emphasizing strengths in ecosystem maturity, custom GPTs, DALL-E image generation, and voice mode, while noting weaknesses in factual reliability without grounding and usage restrictions on premium features
- Claude: highlighting strengths in long-context management, nuanced reasoning, long-form writing, and analytical clarity, while noting limitations in tool ecosystem breadth and image generation
- Selecting the optimal tool based on task requirements, audience needs, or compliance constraints
- A comparative demonstration of identical prompts across all four assistants
Principles of Effective Prompt Design
- Clarity, specificity, and context as the core elements of a successful prompt
- Structuring instructions, tone, format, and constraints effectively
- Identifying common beginner errors and strategies to avoid them
- Refining weak prompts into high-performance instructions through iteration
Zero-Shot, One-Shot, and Few-Shot Prompting
- Distinguishing between the three prompting approaches and determining their best use cases
- Interpreting model behavior to adjust provided examples
- Training a model on new tasks using a small set of carefully selected samples
- Hands-on exercises using ChatGPT, Copilot, Gemini, and Claude
Advanced Prompt Engineering Techniques
- Utilizing conditional and context-aware prompts for more 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
- Understanding few-shot fine-tuning and how it differs from full model training
- Adapting models to specialized tasks using example-driven prompts
- Determining when prompt engineering is sufficient versus when fine-tuning is a better investment
- Evaluating output quality and refining iteratively
Hyper-Realistic Text Generation
- Generating text with precise control over tone, voice, and length
- Creating long-form content, summaries, reports, and structured documents
- Maintaining coherence across multi-step generation processes
- Combining prompt patterns for consistent, brand-aligned outcomes
Applying Prompt Engineering to Business Workflows
- Automating routine drafting, research, and information triage
- Exploring applications in customer support and chatbot development
- Designing reusable prompt templates for teams without 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
- Using negative prompts, weighting, and iterative refinement
- Performing image-to-image transformations 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
- Applications 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 using prompt sequences
- Integrating 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 integrate reasoning across text, image, audio, and video
- Building end-to-end content pipelines without coding
- 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 for the next 12 months
Requirements
Intended Audience
This course is ideal for marketing, communications, and creative professionals looking to leverage AI for content production. It also suits business operations and customer-facing teams aiming to streamline repetitive interactions using prompt-driven tools. Additionally, it provides a structured, tool-focused entry point for beginners with no prior experience in AI or programming who wish to explore 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
