Your functional QA already covers the UI, data, and logic. The part it can't pass or fail with certainty is the AI: functional QA can confirm tests are green and the feature works — whether the answer is actually right is a separate question, and that's the one we test. The same input can produce a different answer each time, and "correct" is a judgment call. Results from week one.
Black-box
testing on the integration layer - no model access needed
Every update
re-tested when the model, prompt, or retrieval logic changes
Human-reviewed
every judgment-call finding signed off by a QA engineer
Added, not separate
runs inside your existing QA engagement
Trusted by teams shipping AI to production
AI product testing is a set of QA checks for the parts of your product that run on a generative AI or LLM component — chatbots, AI search, content generation engines, recommendation systems, or in-app assistants. We add it as a dedicated layer inside your existing product QA, not a separate project.
Unlike functional testing, it evaluates non-deterministic outputs: accuracy, safety, context retention, and output quality across a wide range of real-world conditions. The same input can produce different responses each time, and “correct” is often a matter of judgment — not a binary pass/fail.
That’s why it needs engineers who evaluate what the AI says, not just whether the request technically succeeded.
| Deterministic part | AI part | |
|---|---|---|
| Output | Fixed, repeatable | A valid range of answers |
| Pass / fail | Binary | Judgment against agreed criteria |
| What's checked | UI, data, business logic | Accuracy, safety, context, tone |
| Re-test trigger | Code change | Model, prompt, or data change |
AI product testing doesn’t replace functional, automation, or regression testing. It sits on top of them, covering the one thing they can’t: whether the AI’s answers are actually right. We can run it as a focused engagement, or as part of full-stack QA.
Most teams already have functional and automated QA in place. What they’re missing is a dedicated check on AI output quality – accuracy, safety, context, and tone. That’s the layer we add.
Because it plugs into the QA you already run, findings feed straight into your existing bug-tracking and release process. No parallel workflow, no separate tooling to learn. And if you need the rest of the stack too, the same team covers it.
Accuracy, hallucination, safety, context, tone
Scripts, CI/CD, flakiness, coverage gaps
UI, data, business logic, API contracts
Load, pen-testing, WCAG compliance
We integrate LLMs into our own tooling - and test them the same way we test yours.
QA Madness engineers build AI features into our own products, including QAM Hub, our test management platform. We hit the same hallucinations, prompt regressions, and broken outputs as builders, then break them systematically as QA – the same rigour behind our accessibility testing practice. That dual view, building and testing, is what a generalist QA team applying standard test cases to a non-deterministic system doesn’t have.
Chatbot, AI assistant, LLM-powered search, or content generation — and you've done functional QA but haven't validated what the AI actually says.
Even a minor prompt change can alter how the AI handles edge cases and safety boundaries. Silent regressions are the most common post-update failure mode.
Healthcare, finance, legal, or enterprise. One bad response can mean regulatory exposure, user harm, or a reputational incident that takes months to recover from.
Regulators and enterprise procurement increasingly ask for documentation of due diligence. Structured AI testing provides that evidence before it becomes a blocker.
Six coverage areas - each addressing a failure mode standard test cases aren't designed to check.
We validate that the AI correctly understands user requests, maintains context across multi-turn conversations, and applies your product knowledge, brand tone, and terminology consistently - not just on the first message.
We evaluate whether AI responses are correct, relevant, and complete - with specific focus on hallucination risk. If the AI is confidently inventing prices, features, or policies, we find it before your users do.
We verify that the AI refuses harmful requests, protects sensitive data, avoids giving advice it shouldn't, and resists prompt injection attacks. One bad response can go viral.
We confirm the AI returns results in the correct format - valid JSON, proper markdown, appropriate length, right tone of voice - so nothing breaks in the systems or interfaces that consume it.
Real users don't write clean prompts. We test conflicting instructions, off-topic requests, typos, multi-language input, emotional messages, and deliberate attempts to break the AI.
When the model, system prompt, or retrieval logic is updated, we retest affected scenarios to confirm previously stable behaviour hasn't shifted. Even a minor prompt change can alter edge case handling.
Where the AI answers from your own content, we test the retrieval layer separately from the model: whether the right source was found, whether the answer is actually grounded in it, and what happens when the index is stale or the source is missing. A correct-sounding answer built on the wrong document is still a defect
For assistants that take actions - calling APIs, creating tickets, updating records - we test whether the right tool is called with the right arguments, and what happens when a step fails mid-chain, and whether the agent can be talked into an action it shouldn't take.
Black-box, on the integration layer - no access to your model weights or training data required. We test the AI the way your users hit it.
For each scenario we define what a good answer looks like for your product, then evaluate outputs against it — so "correct" is a documented standard, not an opinion.
A maintained library of jailbreak, prompt- injection, and off-policy inputs, extended with prompts specific to your domain and data.
Automated scoring runs hundreds of prompt variants per scenario; a QA engineer reviews and signs off every judgment-call finding. Scale without trusting a model to grade itself.
We test memory, topic switches, and contradictions across multi-turn dialogues — not just single-shot prompts.
Where you already run automated tests, we can add checks that use a separate model to grade the AI's answers automatically - flagging drift the moment a model or prompt changes, before it reaches users.
Input → actual output → expected → severity and business impact. Each finding can be reproduced step-by-step by your team.
Automated scoring gives us coverage at scale; every result that requires judgment is reviewed and signed off by a QA engineer before it reaches your report. We don’t let a model grade its own output unchecked.
No surprises. Clear milestones. Full transparency at every stage – from kick off to final report
We learn about your product, your AI components, and the quality concerns your team needs answered. You get an honest assessment of what testing scope makes sense — and what you don't need.
Engineers review your product documentation, AI system instructions, intended use cases, and known risk areas. We identify the highest-priority scenarios, define what "good output" looks like for your specific product, and align scope with your release timeline.
Engineers design the specific prompts, inputs, and scenarios — standard use cases, edge cases, adversarial inputs, and scenarios targeting known LLM failure modes: hallucination, context loss, safety bypasses, and output formatting errors.
Engineers run the AI product through all defined scenarios, evaluating outputs against agreed criteria. Every finding is documented with the exact input, the actual output, the expected output, and a severity and business impact assessment.
After your team addresses reported findings, QA engineers retest affected scenarios and run regression checks. For project-based engagements, the full scenario library and regression suite are handed over for your team to reuse. For embedded or ongoing engagements, we keep maintaining and re-running the suite as your product evolves.
Case Study
Customer-facing assistant (LLM via API + RAG over the help centre) for a mid-market SaaS. Functional QA was green; the team had no read on answer quality before launch.
hallucinated answers - invented pricing tiers and a refund window that doesn't exist
prompt-injection bypasses - assistant leaked its system instructions on crafted input
of multi-turn chats lost context - forgot the product named two turns earlier
malformed-JSON outputs - that broke the downstream ticket-creation step
Outcome.
A ~3-week engagement added to the existing QA scope – 140 scenarios across 6 failure modes. All high-severity findings were fixed before public launch, and the scenario library was handed over for reuse on the next model update.
Case Study
Every issue is documented so your team can reproduce and fix it the same day.
Failure mode Hallucination
Severity: HighFailure mode Prompt injection
Severity: High“QA Madness has established a smooth workflow through effective communication. The team is trustworthy, efficient, and hardworking.”
“Thanks to QA Madness’s efforts, we are able to resolve technical issues and keep our platforms optimized and bug-free.”
“QA Madness was seriously professional. They listened to our needs and gave us the kind of work we expected. As a result of their efforts, we can locate a bug in the test environment, which prevents issues from entering production. I would recommend them, 100%.”
"They’ve always been very professional, prompt, and available when we needed them. We’ve never had any issues or needed to go back and teach them how to meet our standards."
Answered by QA Madness AI testing engineers — what makes AI testing different, how hallucinations are caught, what deliverables you receive, and how to get started.
AI outputs are non-deterministic and open-ended, so correctness is about accuracy, safety and tone, not just pass/fail assertions. We test with that in mind.
It checks whether the model confidently states false information. These are the failures most likely to erode user trust or create liability.
Yes. We test the product behaviour regardless of the underlying model or provider.
We define acceptance criteria with you up front, then assess responses against accuracy, safety, tone and policy.
A clear report of reproducible findings with severity ratings and recommended fixes.
A focused cycle typically runs from kickoff to first findings in week one, with the full report within a single sprint.
No. It is one focused layer that runs alongside your existing functional and regression testing.
Most teams don't know what they're missing until a user finds it. Tell us about your AI product — we'll map the highest-risk scenarios and give you a clear picture of what testing scope makes sense.
