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Preventing AI Hallucinations Is Only Half the Story

· 3 min read
Mike Germain
Mike Germain
Engineering Leader

Someone recently asked me a great question:

How do you prevent AI hallucinations when using language models in sensitive use cases?

Let’s use customer support coaching as a real-world example to illustrate a better framing: not just avoiding hallucinations, but building a system that tells the truth and teaches from it.

The Use Case: Coaching Through Conversation

Imagine you’re analyzing customer support calls to understand why some agents consistently de-escalate tough situations or receive higher satisfaction scores.

At a glance, a language model might:

  • Extract direct quotes tied to turning points in the conversation
  • Summarize those quotes to highlight emotional shifts or key decisions
  • Score sentiment for both the agent and the customer, picking up on cues like confidence, empathy, frustration, or receptiveness

All useful, but if you’re going to trust these insights, let alone coach your team based on them—you need a structure that reinforces trust.

A Pattern That Builds Trust

Here’s a repeatable pattern that blends AI with verification, human judgment, and feedback loops:

  1. Constrain the model’s inputs using Retrieval-Augmented Generation (RAG). This limits the model’s response to the actual transcript, not general knowledge, not vibes.
  2. Prompt the model to extract quotes and summarize only from those quotes. Don’t let it get creative.
  3. Validate the quotes using a tool like grep, cat, or fuzzy matching to confirm that what the model pulled actually appears in the source.
  4. Run sentiment scoring on both the quotes and the summary, for both the agent and the customer.
  5. Compare those scores. If a quote sounds confident but the summary reads as uncertain, that mismatch might signal model drift. Internal consistency is a proxy for trust.
  6. Keep a human-in-the-loop during pilots. Experts review and confirm before you move to scale. No exceptions.
  7. Use validated insights for feedback loops. Highlight what language or tone consistently works, and turn that into coaching signals your team can act on.

From Compliance to Clarity

This pattern doesn’t just reduce hallucinations, it creates clarity.

Quotes give you precision. Summaries give you patterns.

By layering both, you gain the ability to cluster by communication style or emotional tone, even when the words themselves vary.

And when you give teams insights they can see, feel, and trust—they learn faster.

Not because the AI is magic, but because the system is designed to support truth, not just output.

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Why This Matters to Connected Engineering

At its core, this is a Connected Engineering problem.

You’re aligning people, tools, and processes to build confidence in complexity.

You’re turning messy, emotionally loaded conversations into structured feedback.

This is what it looks like when you apply:

  • Connected Understanding — Include people in the loop, especially early.
  • Realness — Show your work. Don’t let models summarize behind a curtain.
  • Creative Tension — Trust systems that surface mismatches. That’s where learning lives.

The question isn’t just “How do we prevent hallucinations?”

It’s “How do we create systems where truth becomes teachable?”