AI Quality: Preventing Confident Lies

Because “it works” is what we say right before something misbehaves.

There’s a moment every AI team knows well: someone finally announces, “It works!” And technically, they’re right. The model produced output. It didn’t crash. It didn’t confess to crimes it didn’t commit. But “it works” is only meaningful if your bar is lying somewhere on the floor.

In reality, AI that simply functions is not enough. AI products must work consistently, safely, predictably, and in a way users actually trust. The days of demo-driven AI, where anything impressive counts as “quality”, are gone. (Such an odd statement for a technology that has only recently become mainstream.) Today, AI needs to be measured like a real product with real stakes.

So while I am currently in the midst of setting these up for my current AI project and they're top of mind, let’s talk about what “quality” actually means in AI, and the metrics that matter far more than “it works.”

Why AI Quality Is Hard to Measure

Traditional software behaves deterministically. Feed it the same inputs; get the same outputs.

AI, on the other hand, behaves like a brilliant but unpredictable intern. Two identical prompts can yield two completely different responses. And sometimes those responses are wrong, weird, overly confident, or, as the industry gently calls it, hallucinated.

This makes measuring AI quality harder. Accuracy is no longer binary. “Done” is subjective. Output correctness varies by task. And user trust becomes both a qualitative and quantitative part of the product experience.

To manage AI effectively, PMs need a new scorecard. One that focuses on outcomes, not vibes.

Here are the metrics that, IMHO, matter most.

1. Hallucination Rate

The_ “please stop making stuff up” _metric as I like to call it.

This measures how often the AI outputs incorrect or entirely fabricated information presented as fact. A low rate is good. Zero is impossible, today.

It’s the single fastest way to lose trust, credibility, and if you’re selling to nonprofits, your reputation with people who have very little patience for nonsense.

2. Accuracy / Correctness

The_ “are we actually right?” metric._

Unlike traditional software, accuracy in AI depends on context. For structured tasks, correctness is clear. For interpretive tasks such as summaries, strategies, recommendations, etc., you need human scoring or rubrics.

Accuracy matters because users don’t pay for interesting answers. They pay for correct ones.

3. Task Completion Rate

The_ “did it do the whole job?” metric._

AI can answer without actually completing the task. For example:

“Draft a donor email” → produces a few bullet points and a poem. I actually saw a colleague use a mainstream AI tool and mid-way through the output, it switched to a story about a princess and a unicorn. Granted, it was a pretty good story, but not in the middle of creating a product strategy.

Task completion measures whether the AI fully did what the user asked and not just something adjacent. (or totally unrelated as was the case with the princess and the unicorn.)

4. p95 Latency

The_ “how long until users open another tab?” metric._

Average latency is meaningless. Customers experience the worst delays, not the typical ones.

p95 latency measures how slow the system is in the worst common cases. Users can forgive slow-ish. They do not forgive unpredictable.

5. User Override Rate

The_ “how often do humans fix the AI?” metric._

Overrides include:

• Edits
• Rewrites
• Undo/redo
• Starting over entirely

High override rate means the AI is creating work, not reducing it.

This metric is pure gold because it tells you where the UX or model is failing in practice. Not in a controlled demo.

6. Confidence Alignment

The_ “is it confident when it should be?” metric._

Two equally dangerous problems:

• Wrong but extremely confident
• Right but apologetic and timid (I refer to the overly apologetic piece as being "too Canadian", and for the record, I am originally from Canada.)

Confidence alignment measures whether the tone matches the accuracy. Poor alignment erodes trust quickly.

7. Prompt Sensitivity / Robustness

The_ “does it break if the user misspells a word?” metric._

Good AI handles:

• Typos
• Redundant instructions
• Unexpected formats
• Ambiguous intent

If small changes to the prompt produce wildly different answers, the system isn’t robust. Au contraire, it’s fragile. And no, "fragile" is not Italian. (Let's see who get's the reference.)

8. Safety Boundary Pass Rate

The_ “does it stay inside the guardrails?” metric._

This measures how consistently the AI avoids:

• Harmful content
• Sensitive personal information
• Biased or discriminatory responses
• Violating permissions or roles
• Sharing system details

One unsafe answer can destroy months of trust. This is the one that REALLY keeps me up at night.

9. Retrieval Quality (for RAG systems)

The_ “did it look at the data, or just guess?” metric._

If your AI uses Retrieval Augmented Generation, this metric is critical. Poor retrieval leads to:

• Irrelevant answers
• Outdated info
• Generic responses
• Hallucinations disguised as confident truth

Great retrieval equals grounded, reliable output.

10. User Trust Score

The_ “will they use it again?” metric._

Trust is the ultimate AI KPI. You measure it through:

• Surveys
• CSAT/NPS specific to AI features
• Usage and retention
• How often users choose the AI feature over manual effort

Trust compounds. Once lost, it’s difficult to regain. "Trust is the easiest thing in the world to lose and the hardest thing in the world to get back" - Robert Williams.

How These Metrics Work Together

No single metric defines AI quality. Together they create a holistic picture:

• Accuracy + hallucination rate → reliability
• Task completion + override rate → usefulness
• Latency + robustness → usability
• Safety + trust → product viability

This moves you from measuring functionality to measuring experience, which is where AI lives or dies.

How to Operationalize AI Quality

Metrics are only valuable when they guide decision-making. Here’s how to make them stick.

Establish Quality Gates Before Every Release

For example:

• Hallucination rate < 3%
• p95 latency < 5 seconds
• Task completion > 85%
• Safety pass rate > 99%

Shipping without gates is how you end up in cleanup mode.

Build an AI Quality Scorecard

A living dashboard with:

• Trends by week
• Comparisons across model versions
• Usage patterns
• Segment-specific behavior

If quality dips, you know immediately.

Instrument Everything

Track:

• Edits
• Regenerations
• Cancellations
• Unusual behaviors
• Where users abandon the AI

This turns user behavior into product insight.

Create a Feedback Loop

Capture feedback, route it to the team, and close the loop. Users forgive imperfect AI when they see it improving.

Test Across User Segments

Different users produce different results. Maybe it works perfectly for simple tasks but breaks for complex ones.

You can’t fix what you can’t see.

Red-Team Continuously

AI is dynamic. New failure modes appear. New prompts surface. New risks emerge.

Red-team like it’s part of your fitness regimen. For those who may not be aware, a red-team is a group that intentionally challenges your organization’s plans, systems, or assumptions by thinking and acting like an adversary. Their job is to probe for weaknesses, technical, strategic, or operational, that insiders may overlook. They simulate real-world attacks or failure scenarios to reveal blind spots and stress-test preparedness. The outcome is sharper decision-making, stronger defenses, and more resilient strategies.

The Future of AI Quality

Soon, AI systems will:

• Detect degraded quality before users do
• Self-correct prompt chains
• Auto-ground responses in authoritative data
• Adjust safety boundaries dynamically
• Personalize quality profiles per user

PMs will shift from “measuring quality” to “guiding intelligent systems that measure themselves.”

Quality management will become proactive, not reactive.

Final Thoughts

Measuring AI quality isn’t optional. It’s the foundation of trust, retention, and long-term product success. “It works” is the bare minimum. Modern AI products must work reliably, predictably, ethically, and delightfully.

When leaders measure what actually matters, accuracy, task completion, trust, safety, and user behavior, they stop shipping AI features and start shipping AI experiences.

Now, I need to get back implementing the framework to capture and report on these metrics in my product.

Wishing all of you the very best.

Mike