Course Introduction
As generative AI tools become standard across workplaces—from public institutions to corporate offices—the challenge is no longer adoption. It’s accountability.
“Fasten Your AI Seatbelt” is a practical course designed for professionals who want to lead with AI, but also think before they prompt. This course takes participants beyond the hype and into the real-world responsibilities of using large language models (LLMs) and AI-powered tools in sensitive, high-impact environments.
You’ll explore the risks of hallucinated outputs, embedded biases, and over-reliance on AI for critical decision-making. You’ll learn how to safeguard personal and official data, choose what content should never be shared with AI systems, and implement internal checks to maintain quality and integrity. Through case studies, scenario walkthroughs, and discussion-driven activities, you’ll see how good intentions can go wrong—and how to design systems and habits that keep AI use aligned with organizational values and ethical expectations.
Whether you’re in government, healthcare, agriculture, education, finance, or just trying to protect your team’s data from ending up in some machine’s long-term memory, this course will equip you with the awareness, the tools, and the caution tape needed to use AI confidently and carefully.
Because when you’re moving fast with powerful tools, the seatbelt isn’t optional—it’s smart leadership.
Course Modules
Module 1: Why Responsibility Matters in the Age of AI
What can AI do vs. what should AI do?
The risks of automation without guardrails
Case studies: harmless prompt → problematic outcome
Module 2: Bias in, Bias out – Understanding AI’s Blind Spots
How AI inherits bias from training data
Real-world bias examples: race, gender, language, location
Spotting subtle bias in everyday outputs
Module 3: Hallucinations and Overconfidence
Why LLMs confidently generate wrong information
Identifying signs of hallucination
Strategies for verifying AI-generated content
Module 4: Data Privacy and Sensible Prompting
What not to share with AI tools: PII, sensitive docs, internal info
Platform limitations (e.g., where your data goes when you type it)
Organizational risks from over-sharing
Module 5: Responsible Document Use and Information Sharing
Deciding which files should be uploaded to AI tools
OpenAI, Google, Microsoft—what they keep, what they learn from
Redaction, summarization, and smart filtering before submission
Module 6: Transparency, Disclosure, and Accountability
When (and how) to disclose AI-generated content
Who’s responsible when AI gets it wrong?
Creating internal guidelines or AI usage policies
Module 7: Ethical Prompt Design and Red Teaming
Writing prompts that avoid harmful or manipulative responses
Thinking through unintended consequences
Red-teaming exercises: “Could this prompt be misused?”
Module 8: Building a Culture of Ethical AI Use
Creating team norms and ground rules
Leading by example: modeling responsible practices
Updating org policy to reflect real AI use
Key Takeaways
By the end of this course, participants will be able to:
Use Generative AI with Awareness, Not Assumption
Recognize the limitations of large language models, including hallucinations and misinformation
Understand the systemic biases present in AI tools—and how to spot them in outputs
Protect Data, Privacy, and Institutional Integrity
Identify what types of personal, confidential, or official information should never be shared with AI tools. Use best practices for prompt safety, document redaction, and platform awareness
This course isn’t about slowing down innovation—it’s about keeping it safe, useful, and human-cantered. Because if you’re going to drive this thing, you’d better wear a seatbelt.
The course is available as an institutional workshop for public sector departments, corporate teams, digital transformation leaders, and innovation-focused organizations.
