When AI Misses the Mark
Even the best AI assistants sometimes fall short. Sometimes the information is outdated. Sometimes the AI misunderstands the question. Sometimes it makes something up that sounds convincing but isn’t even close. That last one is called a hallucination. And yes, it happens… especially when assistants are trained on the world-wide interwebs. (Shoutout to Ardi for admitting when it doesn’t know the answer instead of hallucinating!)
Here’s how to protect trust and keep your assistant on track when things go sideways:
1. Know What “Wrong” Looks Like
Not all misses are the same. Different types of errors need different types of solutions.
Common mistakes:
Outdated info: The answer used to be correct, but isn’t anymore
Hallucination: The assistant generates an answer that sounds real but has no basis in your content
Misunderstanding: The user’s phrasing didn’t match the way the assistant was trained
Chatbot confusion: Users expect real AI and instead get a response that feels scripted or limited
2. Correct the Answer and Let People Know
Fixing the answer is essential, and so is telling the user that you fixed it.
When a user flags a wrong answer:
Thank them for catching it
Correct quickly
Let your team know what happened
Follow up with the user
Real-world example:
An insurance company’s assistant provided incorrect information about open enrollment deadlines. Once corrected, the team sent a short follow-up email to affected users. Satisfaction scores went up, not down. People appreciated the transparency.
3. Fix the Source, Not Just the Surface
If something goes wrong, it usually traces back to the content it was trained on.
To prevent future errors:
Find the document or section that fed the wrong answer
Update the content at the source
Double-check that the assistant reflects the change
This reduces future hallucinations and keeps your content aligned across platforms.
4. Keep Your Team in the Loop
When something breaks, your staff should be the first to know. That way, they can answer questions confidently and redirect members with clarity.
What to share:
What the question was
What the assistant said
Why it was wrong
What has been fixed
What to say if someone brings it up again
Some teams use a ticketing system, Slack, or a shared Google Doc for tracking. However you do it, incorporate into an existing workflow.
5. Reset Expectations if Needed
Many people assume AI provides instant answers to any topic, but most assistants are trained on specific internal data. Not everything is fair game.
If you’re getting unrealistic feedback or off-topic questions, you may need to clarify:
What the assistant is trained on
What it can and cannot do
How it compares to tools like ChatGPT or other AI being used
When you launch, send your users an email to explain what their AI assistant was trained on. This will set expectations and reduce frustration when members try to use it like a search engine.
6. Learn From the Mistake
Every wrong answer is a signal. Use it to improve your assistant over time.
What to log:
The original question
The assistant’s response
The correct answer
The fix you made
Any updates needed to source content
Over time, you’ll start to see patterns. You may even want to create a “high-risk” topic list for sensitive areas like dues, CE, billing, or legal timelines. These deserve a little extra attention.
Final Thought
When AI gets something wrong, you don’t lose trust because of the error. You lose trust if you ignore the error or try to cover it up.
Fix it and fix it fast. Be transparent. Keep learning.
Up Next: The ROI of AI in Real Support
Is your assistant actually saving time, money, and energy? In our next post, we’ll walk through where AI delivers measurable results and how to calculate its value inside your organization.