I’ve been in the Learning and Development trenches for 11 years. I’ve seen the industry pivot from Flash-based modules assessment alignment checklist to mobile-first microlearning, and now, we’re all riding the wave of Generative AI. Over the last 18 months of piloting these tools in my workflow, I’ve learned one immutable truth: AI is a brilliant intern who sometimes decides to make things up to impress you.
If you are using AI to draft your e-learning scripts, video outlines, or assessment questions, you are essentially outsourcing your first draft. That’s fine. But if you aren’t aggressively validating that output, you are essentially outsourcing your professional credibility. When we talk about hallucination checks, we aren't just talking about spotting typos. We are talking about preventing the spread of misinformation in a corporate environment where accuracy is our only currency.
I keep a running "Gotchas" doc—a graveyard of AI-generated misinformation I’ve caught over the last year. It’s saved me from launching modules that cited non-existent policy numbers and invented acronyms that sounded "corporate enough" to pass a lazy eye. Let’s look at how to stop this before it hits the LMS.
1. The Validation Mindset: Risk-Based QA
Not every script needs the same level of scrutiny. If you’re writing a script for a "Welcome to the Company" culture video, the stakes are relatively low. If you’re writing a script for "How to Handle Sensitive Customer Data," the stakes are catastrophic. We need to apply Risk-Based QA to our AI-assisted work.
Before you even open a prompt, classify your content. I use a simple matrix to determine how much of my energy I need to allocate to AI accuracy review.
Content Type Risk Level Validation Strategy Soft Skills (Empathy, Feedback) Low Focus on tone and flow; verify logical consistency. Product Knowledge Medium Strict cross-reference with current product documentation. Compliance/Legal/Process Critical Zero-trust policy. Every claim must be tied to a source.If you treat every script as "High Risk," you’ll burn out. If you treat every script as "Low Risk," you’ll eventually cause a PR disaster. Know your content before you start your audit.
2. Tactical Fact-Checking: Beyond the "Looks Good"
One of my biggest pet peeves in this industry is the "looks good to me" feedback loop. It’s lazy, and it’s dangerous. When reviewing AI-written training scripts, you need to be an active, adversarial reader. You are looking for the places where the AI has "filled in the https://essaymama.org/how-do-i-validate-ai-content-for-regulated-training-topics/ blanks" with plausible-sounding nonsense.
The "Inverse Search" Technique
When the AI provides a statistic, a date, or a technical instruction, do not verify it by searching for that exact string. Instead, search for the source material independently. If the AI claims, "70% of employees prefer remote work," don't search for that sentence. Go to your internal HR survey data or the reliable industry report you *intended* to reference. If the AI's claim doesn't match your source, it is a hallucination.
Source Verification Workflows
To keep my workflow efficient, I use a three-step validation process for any high-stakes script:
Highlight the Anchors: Any time the script mentions a policy number, a law, or a specific technical spec, highlight it in red. This is your "fact-check queue." Map to Documentation: Create a "Traceability Table." Column A is the AI statement. Column B is the internal source document (URL or file name). If you can’t fill in Column B, cut the statement from the script. Reverse-Engineer Assessments: For assessment questions generated by AI, I treat them like a learner trying to break the course. I intentionally try to find a scenario where the "wrong" answer could technically be right. If I can prove the AI's logic is ambiguous, I rewrite it.3. Targeted SME Review: Moving Past "Looks Good to Me"
Subject Matter Experts (SMEs) are busy. If you send them a 15-page script and say, "Let me know what you think," they will glance at it, see that the headers look correct, and reply with "Looks good to me." You have just validated the AI's hallucinations through proxy.
You need to be specific. Change your review request to a targeted review. Instead of a general request, give them a validation worksheet.
The "Targeted Review" Template
- Statement Accuracy: "On page 4, the AI claims our refund policy covers shipping costs. Is this accurate?" Tone Check: "The section on Conflict Resolution sounds a bit robotic. Does this sound like how our managers actually speak, or is it too formal?" The 'Gotcha' Hunt: "I have flagged three specific technical procedures where the AI generated instructions. Please confirm these steps align with the latest SOP."
By forcing the SME to answer specific questions, you remove the chance for them to give you a vague "looks good." You are essentially guiding them to do the hard work of validation for you.
4. The "Gotcha" Doc: Systematizing Your Learning
My "Gotchas" doc is my most valuable asset. Every time I catch an AI error, I log it. This does two things: it helps me recognize patterns in how the LLM hallucinates, and it helps me adjust my prompt engineering.
Common AI "Gotchas" to watch for:
- The "Made-up Acronym": The AI knows you like corporate speak, so it will create a P-L-A-N acronym that doesn't actually exist in your culture. The "Ghost Policy": It will invent a policy that sounds like a standard HR procedure (e.g., "The 48-hour feedback rule") that your company has never actually implemented. The "Confident Incorrect Math": Never trust AI to calculate percentages or budgets in a script. It will confidently tell you that 20% of 500 is 125, and it will sound so sure of itself that you might believe it.
If you see a recurring pattern of hallucination, update your prompt. If the AI keeps inventing statistics, your system prompt should read: "If you do not have the specific statistic in the provided source text, do not invent one. State that the source material does not provide a specific number."
Final Thoughts: You Are the Lead, AI is the Intern
Using AI for L&D isn't about hitting "generate" and pushing to production. It’s about being a better editor. The most effective instructional designers in the age of AI aren't those who write the fastest; they are the ones who validate the hardest.
Stop accepting "looks good to me" as a QA standard. Start obsessing over the details. Rewrite your sentences until they are impossible to misinterpret. And for heaven’s sake, keep a "Gotchas" doc. You’ll be surprised at how much you’ll learn about your own internal processes when you start questioning the machine.
The goal isn't to get the AI to do the work. The goal is to get the AI to do the heavy lifting of drafting, so that you—the human expert—can spend your energy on the actual value: ensuring that our people are learning the right things, the right way.