What you'll learn:
- People who have never used generative AI and want to learn how to ask it things without making mistakes or depending on "luck" to get a good response.
- Ask better prompts and verification questions that force AI to show sources, steps, and assumptions before accepting an answer.
- Apply practical guardrails (formats, structures, and self‑review) to drastically reduce hallucinations without needing any coding skills.
- Integrate AI safely into their workflow by deciding what to delegate, what to always double‑check, and when not to rely on AI for critical decisions.
- Understand how generative models work at a high level (statistical patterns, not logic) and why this creates typical errors like hallucinations.
- Use advanced prompting patterns (Devil’s Advocate, Multi‑View, step‑by‑step reasoning) to get more complete and less biased AI analyses.
- Design prompts with strict formats (lists, tables, JSON) that make responses clearer, auditable, and easier to review as a team.
- Combine human judgment, external sources, and AI output in a layered verification system to work with AI confidently and with traceability.
- Spot warning signs in text, data, and numbers (overconfident tone, vague sources, impossible calculations, strange dates) so they can review in time.
- Create reusable prompt templates with built‑in verification that their team can apply consistently across different projects.
- Evaluate when to use AI only as an assistant (research, drafts, brainstorming) and when expert review is mandatory.
- Document AI use in key projects to keep a clear record of what was delegated, how it was verified, and which decisions the team made.
AI tools like ChatGPT, Claude, and Copilot are now everyday work companions, but they’re also confidently wrong much more often than most people realize. They invent facts, misquote data, fabricate references, and sound completely certain while doing it. This course gives you a practical, non‑technical system to use AI safely and reliably in real professional contexts, whether you’re just starting with AI or already using it daily at work.
You’ll learn why generative models get things wrong (they match patterns, they don’t “think”), what hallucinations really are, and the other common problems you need to watch for, like bias, vague answers, and lost context. Then you’ll train your eye to spot red flags in seconds: overconfident tone, missing or fuzzy sources, impossible calculations, strange dates, and too‑perfect statistics.
From there, you’ll practice simple but powerful questioning techniques: asking for sources, step‑by‑step reasoning, alternatives, assumptions, and self‑critique. You’ll also learn how to build guardrails directly into your prompts, strict formats, verification steps, and consistency checks, so the AI does more of the quality control for you.
Finally, you’ll integrate everything into a safe workflow: what to delegate to AI, what to always verify, when never to trust AI alone, and how to combine AI with human judgment and external sources. A hands‑on workshop lets you analyze and fix real AI responses so you leave with practical, reusable habits for safe, professional‑grade AI use.
