AI isn’t just another technology trend, it’s a major shift in how software behaves, evolves, and fails. For decades, we built our testing strategies on a simple foundation: determinism. Given X, the system returns Y. Every time. Predictability was our friend.
And then GenAI happened.
Today, we’re testing systems whose outputs change with every execution, even when the inputs don’t. Systems that “reason.” Systems that learn the wrong things as easily as they learn the right ones. Systems that fail in ways no UI automation script will reliably catch. These models operate with a degree of freedom and unpredictability that looks less like traditional software and more like… well… a beast that needs taming.
This post outlines how we regain confidence in that unpredictability, based on the framework from my conference session Taming the Beast: Testing the Unpredictable with Confidence and updated with the latest industry practices and the lessons learned from real-world AI engagements across our clients.
In a traditional system, variation is usually a bug. In a GenAI system, variation is a feature.
Given the non-deterministic nature of AI, even a static prompt can produce wildly different outputs, including different structure, tone, phrasing, and occasionally correctness. That’s the heart of the challenge:
If every output is different, how do we know if any of them are correct?
This is the inversion GenAI forces on quality engineering. Instead of verifying exact expected results, we now evaluate:
Testing has shifted from “assert” to “assess.”
One of the most important realizations for quality engineers is recognizing that AI systems introduce unique quality risks at every stage, from data acquisition to model monitoring. And these risks are expanding dramatically as organizations adopt:
The days of “test the UI and move on” are gone.
Modern QA must now account for:
GenAI testing isn’t just a testing discipline. It’s a cross-lifecycle risk discipline.
Here are some cornerstone techniques we’ve found helpful in testing non-deterministic systems:
While human-in-the loop evaluation can increase our confidence in non-deterministic output, it’s simply not scalable. To address this concern, we need to incorporate checking non-deterministic output in our existing automated test suites. A technique we’ve been employing more and more is using LLMs to evaluate GenAI outputs. This is becoming one of the most practical breakthroughs in AI quality engineering.
Modern testing harnesses increasingly rely on LLM evaluators because:
However, we’ve learned a few essential lessons:
This combination delivers a repeatable, scalable, cheaper-than-human way to judge AI systems at industrial scale.
Let’s take a look at a couple of examples of the challenges we face in testing AI-enabled systems.
We still see the same issues every day:
With today’s multimodal agents, the scope expands to include:
Testing must now validate both semantic correctness and interaction quality.
Bias testing has evolved radically. Today’s evaluation includes:
This is not optional. It’s essential.
Foundational testing skills form a solid foundation for testing AI enabled systems, but Quality Engineers now need to add the following:
QE is now an orchestration role as much as an execution role.
Absolutely. But not with yesterday’s tools, scripts, or expectations.
The goal isn’t perfect control, rather it’s stable confidence in a system that is, by design, variable.
To tame the beast:
AI systems won’t get simpler. But our testing approaches can become smarter, faster, and more aligned with how these systems truly work.
And that’s the key: AI-enabled systems don’t break like traditional software, so we shouldn’t test them like traditional software. Our job is not to force determinism onto a non-deterministic system. Our job is to build confidence in its behavior under uncertainty.
That’s how we tame the beast.
AI-enabled systems demand a new level of rigor, discipline, and observability—far beyond what traditional QE practices were designed to handle. Whether you’re building GenAI features, integrating LLMs into existing workflows, or modernizing your quality engineering function, the risks are real and so are the opportunities for competitive advantage when quality is done right.
At Forte Group, we help organizations:
Test AI-Enabled and AI-Augmented Systems
Implement AI-Augmented Quality Engineering Practices
If you're looking to de-risk AI adoption, raise the maturity of your QE function, or embed AI into your testing practice with confidence, we can help.
Let’s talk about how to tame the beast in your environment. Reach out to me directly, or visit fortegrp.com to learn more about our AI Quality Engineering offerings.
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