AI Wrote Training Examples That Don’t Match Our Company—What Do I Do?
After a decade in Learning & Development, I’ve seen every iteration of "the next big thing." From the rise of SCORM-compliant authoring tools to the frantic transition to fully remote onboarding, I’ve learned one immutable truth: Efficiency is worthless if the content is wrong.
Lately, the buzz has shifted to Generative AI. We are all using it to draft facilitator guides, build scenario-based assessments, and generate localized training content. But there is a glaring problem: AI often delivers examples that sound authoritative but are fundamentally incompatible with the nuances of your organization. When you see a compliance scenario involving a software stack your company doesn’t use, or a tone that sounds like a Silicon Valley startup when you’re a 100-year-old financial institution, the immediate panic sets in.
Stop. Take a breath. Don’t just hit "regenerate" and hope for the best. Here is how to fix the mismatch without losing your mind or your credibility.
1. Apply the "What’s the Risk?" Filter
Before you dive into example rewriting, you must triage the content based on risk. I keep a mental (and sometimes physical) grid for this. If the AI hallucinates a policy detail in a product training manual, that’s an annoyance. If it gets a GDPR compliance requirement wrong, that’s a liability.
Risk Level Content Type Validation Strategy Low Soft skills, non-compliance topics, general professional development. "Sense-check" by two team members; quick scan for tone/cultural alignment. Medium Company-specific product features, internal processes, non-critical SOPs. Direct SME (Subject Matter Expert) review for technical accuracy. High Compliance, security protocols, legal policies, safety procedures. Formal Legal/InfoSec sign-off; mandatory fact-checking against source documentation.
Ask yourself: If this content is wrong, what is the consequence? If the answer involves a fine, a security breach, or a damaged reputation, your workflow cannot be "AI https://www.reddit.com/r/LearningDevelopment/comments/1u9m41z/has_anyone_changed_how_they_validate_aigenerated/ draft -> Publish." It must be "AI draft -> Internal Audit -> SME Validation -> Legal/Compliance Review."
2. Elevate Context Accuracy Through "Seed Data"
The primary reason AI produces irrelevant examples is that it lacks your internal context. If you prompt, "Write a scenario for a salesperson dealing with a difficult client," you will get a generic, useless script. You need to provide the AI with context accuracy.
Stop asking the AI to "write." Start asking the AI to "rephrase based on the following source."
- Provide the Internal Policy: Paste the actual policy text into the prompt.
- Define the Persona: "Act as a mid-level manager at [Company Name]. Use our internal terminology for [Product X] and avoid industry jargon we don't use."
- Establish the Constraints: Tell the AI what it cannot say. For example, "Do not use terms like 'disruptive' or 'synergy'—we hate those words."
3. The Hallucination Log: Your Best Defense
I maintain a personal "Hallucination Log." It’s exactly what it sounds like: a running document of every time an AI model made up a policy, a feature that doesn't exist, or a fake regulation. When you catch an error, add it to the list. Use this log to educate your team on what to watch out for.
Hallucination Detection Tips:
- The "Confidence Trap": AI is most confident when it is most wrong. Never assume accuracy based on the "professional tone."
- Check the Citation: If the AI mentions a policy, check the internal repository. If the AI can't link back to a specific document or link, mark it as a high-risk hallucination.
- Cross-Verify Logic: Does the AI’s scenario follow the logic flow of your internal processes? If the AI skips a step (e.g., "Step 1: Contact IT. Step 3: Resolution"), it has likely invented the middle step.
4. Rethinking SME Validation
One of my biggest pet peeves is the vague "Looks good to me" feedback from SMEs. It’s performative and dangerous. If you send an SME a 50-page document and ask them to "review for accuracy," they will inevitably glance at the first three pages and ignore the rest.
To get SME validation that actually sticks, you have to guide their eyes:
- Targeted Questions: Don't ask, "Is this right?" Ask, "In section 4, does this process accurately reflect our current workflow for [specific software]?"
- The "Red Pen" Expectation: Tell your SMEs that "looks good" is an unacceptable feedback result. Require them to confirm why the content is correct by linking back to the source material.
- Owned Content: Every training piece must have a named owner. If a compliance example turns out to be wrong, we need to know exactly who validated that specific section.
5. Localization and Cultural Context
When training is destined for global teams, AI often defaults to US-centric business practices. Localization isn't just about translation; it’s about cultural fit. An example about "closing a deal in 15 minutes" might work in New York but fall flat in markets where relationship-building takes months.
When reviewing localized content, ensure your regional stakeholders aren't just checking language proficiency—they should be checking for situational relevance. Does the example reflect how business is done in that region? Does the training follow local labor laws? Always have a native stakeholder verify the context, not just the grammar.
Final Thoughts: Don't Ship Passive Content
We are the guardians of our company’s knowledge. Using AI is a massive efficiency boost, but it doesn't absolve us of the responsibility to be precise. I despise passive voice in policies because it hides accountability—and I feel the same way about AI-generated training that isn't properly audited.
If you find the AI examples are hitting the mark about 70% of the time, don't ship that 70%. Ship 100%. Use the AI to generate the skeleton, but the flesh and blood—the nuance, the company values, and the hard-won operational reality—that belongs to you and your SMEs. Be the editor, be the skeptic, and always, always ask: "What is the risk if I am wrong?"
