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.
At QA Wolf, we’ve built black-box test suites for apps that generate text, images, and structured outputs. This guide covers the four test strategies that help us manage the complexity and keep things running smoothly.
#1: Use deterministic checks for unpredictable outputs
Let’s start with the obvious problem: generative models don’t always return the same thing. But some outputs—like structure, layout, or token presence—can still be tested deterministically if you constrain the randomness and know where to look.
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. 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 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. This means:
- Extracting coordinates and dimensions with JavaScript methods like getBoundingClientRect() or reading the canvas pixel buffer.
- 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.
Prompt filters with allow/deny 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. That’s why it's also helpful to 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.
#2: Use an external judge for subjective checks
Sometimes you can’t write an exact match test. It could be a text, a chart, a voice clip, or a UI block—anything where correctness depends on context or interpretation. In those cases, you can use an external oracle: another model, or a custom scoring script, that provides a structured second opinion.
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.
Rubric scoring: 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.
#3: Stabilize your suite: flake control
Flaky tests ruin velocity. And generative apps flake a lot—because models drift, outputs vary, and infrastructure race conditions creep in. You can mitigate some of that by implementing tighter constraints.
Set a seed: A seed is just a value that tells the model’s RNG 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.
Lower the temperature: 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.
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.
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