After 11 years in L&D—spanning everything from LMS administration and instructional design to leading QA for global enablement teams—I’ve seen a lot of trends. I’ve survived the move from Flash to HTML5, the rise of microlearning, and the inevitable "gamification" wave. But nothing has fundamentally shifted my day-to-day workflow like the last 18 months of piloting generative AI.
Here is the reality check: AI is an incredible junior researcher, but it is a terrible final author. If you’ve been using LLMs to draft your training scripts or e-learning modules, you’ve likely noticed the same thing I have: AI drafts are inherently wordy. They love to fluff, they love to hedge, and they absolutely love to hallucinate "facts" with the confidence of a CEO in a boardroom.
When we talk about editing AI text, we aren’t just proofreading for typos. We are performing surgery on a machine that doesn't understand brevity or instructional intent. If you want to stop wasting time on "looks good to me" feedback and start delivering actual learning results, you need a rigorous validation framework.
Defining Validation: Why AI Isn't Your Subject Matter Expert
In my "Gotchas" document—which I keep to track the common mistakes AI makes so I don’t have to keep fixing them manually—the number one entry is: "The AI assumes the learner knows more than they do, or explains things that don't need explaining."
Validation isn't just checking for accuracy. It’s about checking for pedagogical alignment. Does this text support the learning objective, or is it just filler designed to hit a word count? When you use AI, you have to validate:
- Contextual Accuracy: Is the tone consistent with our company culture, or does it sound like a generic Wikipedia article? Instructional Integrity: Does the content actually teach the skill, or is it just a wall of text? Risk Level: Is this content "high stakes" (e.g., legal, safety, security) or "low stakes" (e.g., team-building, basic software tips)?
Risk-Based QA: Why One Size Does Not Fit All
One of the biggest mistakes I see junior IDs make is treating all content with the same level of scrutiny. You don't have time to deep-dive every single piece of text. Use a risk-based approach to decide how much "fluff cutting" you really need to do.
Content Type Risk Level QA Rigor Editing Focus Compliance/Safety High Multi-stage/Legal Sign-off Total verification; zero ambiguity. Product Knowledge Medium SME Review + ID Check Clarity, accuracy, and flow. Professional Skills Low/Medium Peer Review Conciseness and engagement. Culture/General Comms Low Automated/Quick Scan Tone and brevity.If you are drafting a module on workplace harassment, your editing process should be surgical. If you are drafting a module on "How to use Slack channels," you can afford a bit more speed. The goal is to avoid over-engineering the QA process for low-stakes content while ensuring high-stakes content is bulletproof.
The Art of Cutting Fluff in Training
AI models are trained on internet data, and the internet is full of "corporate speak." Phrases like "it is important to note that..." or "leverage the synergies of..." are the death of effective learning. My rule for cutting fluff in training is simple: if you remove the sentence and the meaning remains, delete it. Permanently.
Here is my 3-pass editing framework for any AI-generated draft:
The "Redundancy" Pass: Scan for sentences that start with filler phrases. Cut them. AI loves to explain what it is about to explain before it actually explains it. The "Plain Language" Pass: Are there ten-dollar words where two-dollar words would do? If an AI uses "utilize," change it to "use." If it says "facilitate the acquisition of knowledge," change it to "teach." The "Ambiguity" Pass: I rewrite sentences until they can only be interpreted one way. If a learner can "break" an assessment question because it was poorly phrased, the fault is entirely on the writer, not the student.Fact-Checking and Source Tracking
This is where I get really annoyed with AI. Overconfident AI outputs with no sources are a liability. If you aren't tracking where your information comes from, you are setting yourself up for an embarrassing internal audit.
When I generate content with AI, I require the model to provide references. If it can't, I treat the text as "suspect." I then perform what I call the "Source-Anchor" method:
- Identify the core claims in the module. Cross-reference each claim against internal documentation, wikis, or policy PDFs. If the AI claims something as a "fact," you must be able to link to the source document in your "Gotchas" or "Reference" file.
If the AI made it up, you need to rewrite it based on the actual source material. Never trust the AI's "synthesis" of a policy document. It will miss the nuance every single time.
Targeted SME Review: Stop Being a Vague Collaborator
Nothing grinds an SME’s gears faster than sending them a 40-page, AI-generated blob of text and saying, "Can you check if this looks good?" They will either ignore you, give you a vague "looks good," or—worse—re-write the whole thing in a way that doesn't fit the learning architecture.
Make your SME review targeted and efficient:
- Highlight specific sections: "I’m confident about X and Y, but I need you to verify the steps in section Z." Use the "Question-First" approach: Don't ask them to "review the draft." Ask, "Does this accurately reflect the current software workflow?" The "Breaker" Test: I tell my SMEs: "I’m going to try to break this assessment. Can you tell me if my interpretation of this rule is incorrect?"
By giving them specific, narrow tasks, you respect their time, and you get high-quality feedback. You become the pilot of the content, not just a relay for the AI’s output.
Final Thoughts: Don't Be an "AI-Pushing" Robot
The danger in L&D right now isn't that AI will take our jobs; the danger is that we’ll become lazy. We’ll become "prompt engineers" who treat AI output like gold, failing to apply the basic clarity checks that make training actually *work*.
We are the last line of defense between the learner and a miserable, confusing, 45-minute slide deck. Keep your "Gotchas" doc updated. Be the person who isn't afraid to say "this is bad" to a machine. And for the love of everything, stop writing in "corporate reddit.com voice." Your learners are humans. Write to them like one.