AI Wrote Training Examples That Don’t Match Our Company: What Do I Do?
I’ve been in the Learning & Development trenches for a decade. I’ve seen every "next big thing" in corporate training come and go, but generative AI hits differently. It’s faster, it’s slick, and it’s arguably the most efficient way to generate "corporate-sounding" fluff I’ve ever seen. But here is the problem: Efficiency without accuracy is just a faster way to ship misinformation.
Last week, I was reviewing a module on internal data handling. The AI had generated a scenario about "accessing the shared drive." It was grammatically perfect. It flowed well. But it described a process that didn’t exist at our company, cited a software version we decommissioned in 2022, and implied that our InfoSec team handles requests that actually route through IT Operations. If a new hire had followed that advice, they would have been locked out of their system within ten minutes. That’s when I added that entry to my "hallucination log"—a running, slightly cynical record of every time AI has confidently lied to us.
If you are staring at AI-generated training material and wondering why it feels "off," you are right. AI is a prediction engine, not a company employee. It doesn't know your culture, your legacy systems, or your specific internal jargon. Here is how you fix it.
1. Start with the "What’s the Risk" Test
Before you fix a single sentence, you must apply the most important question in my toolkit: What is the risk if this is wrong?
I hate performative paperwork—the kind of review cycles that exist just to say we "did a review." But when it comes to AI, you need a risk-based validation strategy. Not all content deserves the same level of scrutiny. If the AI hallucinates a typo in a fun "welcome to the team" video, it’s annoying. If it hallucinates a step in a compliance policy or a safety protocol, you are looking at potential litigation, audits, or workplace accidents.
Content Type Risk Level Validation Strategy Corporate Values/Mission Low Peer review for tone; light brand alignment check. Management Best Practices Medium L&D Manager review; check against leadership model. Compliance/Safety/InfoSec High Legal/SME sign-off; manual verification of every citation.
2. Managing Hallucinations: The "Intern" Mindset
peer review vs AI validation tools
Stop treating AI like an expert and start treating it like a hyper-confident, slightly delusional intern who has never stepped foot in your office. The AI will make things up to fill the gaps in its training data—this is what we call a hallucination. When you spot one, log it. My team keeps a shared spreadsheet of "AI Weirdness." It helps us https://fire2020.org/how-to-validate-ai-generated-training-visuals-a-10-year-ld-veterans-guide/ identify patterns, like which prompts consistently lead to the model inventing nonexistent company policies.
How to detect and prevent them:
- Constraint-based prompting: Do not ask the AI to "write training for a sales rep." Tell the AI: "Use ONLY the attached policy document as your source. If the answer is not in the document, state that you do not have the information."
- Citation forcing: Always force the AI to provide a source for its claims. If it can't cite a specific document or page number from your internal knowledge base, you cannot trust the example.
- Fact-Checking Loops: If you are writing a high-stakes policy, treat the AI output as a draft. You must verify every single claim against your source of truth (e.g., your Employee Handbook or Tech Stack documentation).
3. Mastering Example Rewriting for Context Accuracy
AI struggles with internal context. It knows what a "project manager" is in the abstract, but it doesn't know how your specific project managers communicate in Slack or how your internal ticketing system works. When the AI gives you generic, lifeless examples, you have to perform a surgical rewrite.
Example Rewriting Tips:
- Strip the Generics: Remove phrases like "in the fast-paced world of..." or "it is essential to remember..." These are AI filler words that scream "I didn't actually read your culture."
- Inject Real Data: Swap the AI’s placeholder names for real team names or specific internal systems. If the AI suggests "Update the CRM," change it to "Log the lead in Salesforce and trigger the 'New Account' workflow."
- Localize the Tone: Does your company use "folks," "team members," or "colleagues"? Ensure the examples match the way people actually talk in your hallways. If the AI uses passive voice, kill it. Active voice creates clear accountability—something AI rarely prioritizes.
4. SME Validation: Stop Saying "Looks Good to Me"
I have a visceral reaction to "looks good to me" feedback. It’s the death of quality assurance. When you bring an SME into the loop to review AI-generated content, you must design the review process to force actual engagement. If you send a 40-page PDF, they will scan it, miss the mistakes, and sign off because they are busy.
Designing a Better SME Review:
Break the content into small, high-stakes chunks. Create a specific checklist for the SME rather than asking for general feedback.
Your SME Review Checklist:
- Does this scenario accurately reflect our current workflow? (Yes/No)
- Are the technical terms used in the correct context? (Yes/No)
- Would a new employee actually be able to execute this task based on this description? (Yes/No)
- Is there any information here that violates our current policy? (Yes/No)
If they answer "No" to any of these, require a comment explaining the fix. This turns the SME review from a "rubber stamp" task into an active validation session.
5. The Importance of Ownership
Finally, we have to talk about accountability. The biggest danger of AI in L&D is the "diffusion of responsibility." When an AI writes a bad training module, people blame the tool. But the tool isn't the one shipping the content—you are.
Every piece of AI-assisted content must have a named owner. If you are the L&D practitioner responsible for that module, your name goes on it. You are the final line of defense against inaccuracy. If you don't have the time to verify the content, you don't have the time to ship it. It is that simple.
AI is a tool, not a teammate. It can help you draft faster, but it cannot take the place of your deep knowledge of the company’s systems, culture, and compliance requirements. By implementing a risk-based validation strategy, logging hallucinations, and forcing meaningful SME engagement, you can leverage AI without compromising the integrity of your training.
Now, go check that last draft. I bet there’s a hallucination hiding in paragraph three.
