Every SaaS roadmap now has an AI feature on it — a chat assistant, a summarizer, an agent that acts on user data. And every team shipping one discovers the same problem: traditional QA breaks down when the same input can produce a different output on every run.
You cannot assert equality on non-deterministic responses. But that does not mean LLM features are untestable — it means they need a different test strategy. Here is the playbook QaLock uses for AI-powered SaaS products.
Separate the deterministic shell from the probabilistic core
Most of an AI feature is still deterministic: the prompt template, the retrieval pipeline, the input validation, the output parsing, the fallback when the model times out. Test all of that with standard Playwright and API suites — it is where the majority of production incidents actually originate.
The probabilistic core — the model response itself — gets a different treatment: property-based assertions. Do not check the exact wording; check that the response is valid JSON, cites a real document, stays under the length limit, and never leaks another tenant's data.
Build an eval suite before you swap models
An eval suite is a versioned set of representative inputs with scored expectations — accuracy on known answers, refusal on unsafe requests, format compliance. Run it in CI on every prompt change and every model upgrade, exactly like a regression suite.
Teams that skip this discover regressions the expensive way: a model upgrade silently changes tone, breaks JSON output, or starts answering questions it previously refused. A 100-case eval suite catches this in minutes.
Test the guardrails, not just the happy path
Prompt injection attempts, jailbreak phrasing, off-topic requests, oversized inputs, and adversarial follow-ups all belong in your regression suite. If your assistant can access user data, test cross-tenant isolation aggressively — it is the highest-severity failure mode an AI feature can have.
QaLock runs structured AI security QA alongside functional automation — covering injection resistance, data leakage, and output safety with documented evidence your enterprise clients can review.
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