Course Objective & Scope
As AI tools become standard across enterprises, misunderstandings about how models operate often lead to poor outputs or data exposure. This program demystifies rule-based automation, predictive machine learning, and generative large language models (ChatGPT, Claude, Gemini, Microsoft Copilot) in clear, non-technical terms.
Participants explore core concepts such as tokens, context windows, probabilistic generation, and hallucinations through realistic workplace scenarios. In customized hands-on labs, teams build a role-based safe usage checklist and a task suitability matrix aligned with organizational policies.
Who Should Attend?
- Business professionals and operational teams looking to use AI tools confidently and safely in daily work
- Team leads and department managers aiming to establish a shared AI vocabulary across their units
- Specialists in HR, corporate communications, finance, procurement, and customer operations
- No coding or technical background is required
Key Takeaways
- Distinguish between traditional automation, predictive machine learning, and generative AI for business tasks
- Identify the capabilities, limitations, and hallucination patterns of large language models
- Formulate clear prompts for drafting, summarizing, and structured brainstorming in everyday workflows
- Apply corporate data privacy, intellectual property, and security rules when interacting with AI services
- Verify AI-generated outputs using structured human-in-the-loop review and source checking
Curriculum
AI Concept Map and How Models Work
- Distinguishing rule-based software, machine learning, deep learning, and generative AI
- How large language models (LLMs) predict tokens probabilistically rather than retrieving facts
- Overview of commercial AI assistants and enterprise model ecosystems
Foundational Interaction and Prompting in Daily Work
- Defining context, objective, and output format for reliable model responses
- Understanding context windows and why models lose track in long conversations
- Core task patterns for summarization, rewriting, classification, and ideation
Model Limitations, Hallucinations, and Bias
- Non-technical breakdown of why models generate plausible-sounding falsehoods (hallucinations)
- Training data biases, knowledge cutoffs, and common reasoning or calculation pitfalls
- Evaluating which tasks suit AI assistance versus strict human decision-making
Safe, Ethical, and Responsible Enterprise Usage
- Red lines for personal data, trade secrets, and confidential client information
- Human-in-the-loop governance and accountability for published or shared outputs
- Workshop: Building a role-based safe usage checklist and task suitability matrix
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