Why testing AI applications is different

Testing generative AI apps comes with a different set of rules. They’re unpredictable by design—outputs can shift with the same input, and each model run consumes compute, which adds cost, especially when generating large outputs, using high token limits, or running frequent tests across CI pipelines. That makes it challenging to apply the usual regression testing playbook. But if your team is shipping generative AI features, you still need to catch regressions, validate edge cases, and do it all without blowing your infrastructure budget or slowing down deployments.

This guide breaks down three techniques QA Wolf uses to test generative AI applications: deterministic assertions on structured output and DOM or canvas layout, constrained external judges for validating subjective output, and flake control methods that reduce retries and keep CI reliable. Each technique maps to a different failure mode teams hit when testing LLM-backed features and makes those checks stable enough to run continuously.

Creating deterministic checks for unpredictable outputs

Testing generative AI outputs requires deterministic checks that focus on structure rather than exact content. By constraining randomness with seeds and temperature settings, you can reliably validate things that should not change—such as data schemas, DOM elements, layout signals, or required tokens—even when the wording varies.

Make assertions on structured data

If the AI-generated output can be exported as structured data, such as an SVG or XML, a deterministic test can validate that specific markers are present.

We store a known-good output and compare new responses against it—a baseline/known-good output strategy that ensures explicit validation over opaque guessing. This only works if:

• You fix the model seed or lower the temperature
• The schema stays consistent
• You normalize things like whitespace and number formatting before comparison

We've used this approach to validate config generators, UI state outputs, and API payloads from prompt-based tools.

Check canvas elements programmatically

If your app renders charts, drawings, or other canvas elements from generative AI output, you can validate the output by programmatically inspecting the DOM or canvas API—following the lifecycle of a test helps ensure these checks run reliably in your E2E workflow.

This means extracting coordinates and dimensions with JavaScript methods like getBoundingClientRect() or reading the canvas pixel buffer, then comparing these metrics to known-good ranges or patterns.

This method relies on standard DOM or canvas API access to verify layout and structure. It's brittle if the layout is highly dynamic, but valuable for catching regressions where the number or position of visual elements is predictable.

Stress-test content filters with prompt lists

To validate content moderation, you can stress-test your filters by running high-temperature prompts designed to trigger edge cases.

These prompts aim to surface problematic completions—things like profanity, threats, or banned topics. The test scans the output for any matches against a deny list. If the model slips past the filters, the test fails.

The tricky part is coverage. A deny list that only flags obvious slurs might miss coded language or less explicit violations. Track hit rates over time and adjust the list based on false negatives. This lets your team close coverage gaps without depending on manual review.

Using an external judge for subjective checks

Sometimes you can't write an exact match test (see why exact-match testing breaks down for LLM prompts). The output could be text, a chart, a voice clip, or a UI block—anything where correctness depends on context or interpretation.

In those cases, use an external oracle: another model, or a custom scoring script, that provides a structured second opinion.

Use multiple-choice validation

Say your app generates UI copy or user responses. You can send that output to another LLM and ask it, "Which of these four categories does this fit?"

You already know the right answer. If the oracle picks wrong, the test fails.

This approach works because you're constraining the judge's output to a fixed set of options, which makes the validation deterministic even when the original AI output isn't.

Use rubric scoring for tone and coherence

For things like tone, structure, or coherence, you define a rubric—"score this from 1 to 5."

Then you template that into a prompt and have another model rate the output. We adjust the scorer's seed and temperature to maintain stability and perform sanity checks using examples we've graded ourselves.

This is how we structure brand voice checks and educational content quality so that they can be pre-screened by automation before being reviewed by humans.

Reduce the randomness of generative AI outputs

As we discussed above, the randomness of genAI outputs is what makes them difficult to test. You can mitigate some of that by implementing tighter constraints in the application itself. You’ll need to work with the product developer to make the product more easily testable. 

Set a seed and lower temperature

A seed is a value that tells the model's random number generator where to start. Pass the same seed and prompt, and you'll get the same output.

We set seeds in POST bodies or global configs, never in the UI. This ensures reproducibility across test runs without affecting the production experience.

Temperature controls randomness. At 0, the model just returns the most likely next token. We usually set this around 0.1–0.2 for test paths where we want predictable output.

It keeps things stable enough that oracles and assertions won't fail at random, while still allowing the model to function as designed.

Want a suite that can handle this?

QA Wolf builds, runs, and maintains test suites for LLM-backed apps. We've figured out how to lock down randomness, validate subjective output, and scale test coverage without compromising your deployment speed. Let's chat.