Understanding AI Thresholds: Why HR Tech Leaders Need to Care
If you’re running HR systems that now include AI, you’re already living with the effects of these thresholds whether you know it or not.
AI systems don’t just “work” out of the box. A few hidden thresholds determine whether they sound reliable or unhinged. Most HR leaders never hear about them, but they should. A technical article by Bartosz Mikulski breaks these down. What follows is a translation into simpler terms, and why they matter when you’re deciding how AI shows up inside your HR technology.
The Technical Terms, Broken Down
AI engineers use words like temperature, top-k, and top-p (nucleus sampling) to describe how a system decides what to say next. To most of us, those terms don’t mean much. But they’re just dials that control variety versus predictability.
The easiest way to picture it is through an ice cream counter.
Temperature is the scooper’s adventurousness. Low temperature? You always get vanilla or chocolate. High temperature? You might get jalapeño lime or bubblegum bacon swirl.
Top-k is how many tubs are open at once. If k=1, only the top flavor is available. If k=10, you’ve got ten tubs uncovered, which makes for more variety but also more risk.
Top-p (nucleus sampling) is like saying, “keep uncovering tubs until these options add up to 90% of customer preference.” Some days that’s three flavors, other days five.
These technical settings, invisible in a product demo, determine whether you get safe and predictable output, creative variety, or something strange.
Why This Matters for HR Technology
Too low a temperature and AI-generated job descriptions or coaching tips all sound identical.
Too high and you risk hallucinations in compliance or payroll guidance.
A tight top-k may exclude good but less common answers.
A loose top-p may overwhelm employees with variety.
For HR, reliability usually outweighs creativity. But if the system is too rigid, employees tune it out.
Error Is Built In
Even if the training data were flawless, some error rate is unavoidable. Researchers have shown that generative models always inherit a baseline level of wrong answers, the same way HR reports sometimes produce rounding discrepancies no matter how clean the source data is.
The lesson: no amount of knob-turning eliminates hallucinations entirely. Leaders need to design around that inevitability.
Why Evaluation Makes It Worse
Benchmarks often score AI like a standardized test: guessing gets credit, silence gets nothing. So models learn to answer confidently even when uncertain.
In HR, that’s a liability. If an AI doesn’t know whether an overtime rule applies, the right behavior is to:
say “I don’t know,”
escalate to a human,
or pull directly from the source system.
When you see a demo where the AI never admits uncertainty, treat that as a warning sign, not a selling point.
What Leaders Should Ask
When reviewing AI-enabled HR systems, push vendors on two tiers of questions:
Mission-Critical (payroll, compliance, leave):
Can the system abstain or escalate when uncertain?
How are hallucinations tested in these domains?
Are confidence scores exposed to admins?
Advisory/Engagement (learning, coaching, wellness):
Can thresholds be tuned differently here, where creativity adds value?
How are defaults set, and who controls them?
The Broader Point
The knobs that shape AI output aren’t visible, but their effects are. They determine whether your employees see an assistant that is consistent and trustworthy or one that confuses and frustrates.
Trust in AI doesn’t come from assuming the vendor set the dials right. It comes from knowing some error is inevitable, demanding safe abstention over confident guessing, and asking the questions that reveal how those invisible thresholds are managed.