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Section 1: The Illusion of Competence

Why AI Fails Differently Than We Do

Don't Let Polished Writing Fool You

Section 2: Developing Your Verification Reflex

Building Your Verification Reflex

Catching the Hidden Details That Trip Up AI

Section 3: Frameworks for Quality Judgment

Building a Simple, Bulletproof Evaluation Rubric

Setting the Standard for Your Team's AI Work

Section 4: Pressure-Testing with Red Teaming

Playing Devil's Advocate: How to Red Team AI

Pressure-Testing Deliverables Before They Ship

Section 5: Knowledge Checks

Quiz: AI Failure Modes vs. Human Error

Quiz: The Verification Reflex

Quiz: Identifying Edge Cases

Quiz: Using the Evaluation Rubric

Quiz: Red Teaming in Practice

Section 6: Course Feedback

Module 3 Feedback Survey

In the era of generative AI, producing content is easier and faster than ever before. But how do you ensure that what your AI generates is actually accurate, logical, and safe to use.

In the era of generative AI, producing content is easier and faster than ever before. But how do you ensure that what your AI generates is actually accurate, logical, and safe to use? "Trust but Verify: Mastering AI Quality and Evaluation" is a comprehensive course designed to teach you how to critically assess AI-generated deliverables. AI systems often present highly inaccurate information with absolute linguistic confidence—a phenomenon known as machine hallucination. This course will help you pierce through the illusion of competence created by polished writing and perfect grammar. You will learn to develop a mandatory "verification reflex," a mental pause that prevents embarrassing professional errors. We will guide you through building a bulletproof evaluation rubric based on four core pillars: Factual Accuracy, Contextual Fit, Logical Flow, and Brand Tone. Furthermore, you'll discover how to set high standards for your team's AI work and master the art of "red teaming"—playing devil's advocate to pressure-test AI drafts before they ship. By understanding why AI fails differently than humans do, you will be better prepared to catch the hidden details and edge cases that trip up even the most advanced language models. Whether you are a manager, editor, or individual contributor, this course equips you with the critical thinking skills and practical frameworks needed to confidently integrate AI into your workflow without compromising on quality. Stop passively reading and start actively fact-checking to protect your brand and reputation.

Module 3: Trust but Verify: Mastering AI Quality and Evaluation

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            <p>We’ve all been there: you prompt an AI, and seconds later, it spits out a perfectly formatted, articulate, and authoritative response. It’s incredibly tempting to just copy, paste, and hit send. However, that polished prose often hides "hallucinations" or logical breakdowns that a human would never make. This course is designed to break the spell of the "Illusion of Competence." We will explore why AI fails in ways that are fundamentally different from human error and why structural fluency—great grammar and a confident tone—is often the biggest trap for a busy manager, editor, or analyst. You’ll learn how to shift your mindset from being a passive reader to an active fact-checker, ensuring that the work you approve or rely on is actually as substantive as it appears.</p>

<p>Moving beyond just spotting obvious errors, we will help you build a professional "verification reflex" to catch the subtle nuances and edge cases that AI frequently misses. Whether you are a consultant refining a report, a manager setting quality standards for your team’s AI usage, or a hiring manager evaluating a candidate’s critical thinking skills, you need a repeatable system to judge quality. We will walk through how to create bulletproof evaluation rubrics and even how to "red team" your own deliverables—intentionally pressure-testing them to find the cracks before they reach a client or stakeholder. By the end of these modules, you won't just be using AI; you'll be mastering the art of high-stakes quality control in an increasingly automated world.</p>
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