Jon Lopinot
CTO at BRKFST
Testing
of AI Products
Test the AI layer your standard QA can't reach. Accuracy, safety, context, and output quality – covered
1
Catch hallucinations and safety issues before users do
2
Cover non–deterministic AI behaviour across hundreds of scenarios
3
Add AI testing coverage to your existing manual or automated QA flow
4
Get findings in 1–3 days from kickoff, documented and ready to act on
Reduce long-term QA costs by up to 40% while increasing test coverage
Get a QA Audit
from our Automation Expert
Our AI Product Testing Services
AI product testing is a specialized QA approach for software that includes an AI component – such as a chatbot, AI–powered search, content generation engine, recommendations system, or in–app assistant. It's not a separate service – it's part of your complete QA flow, manual or automated, covering the layer that standard testing can't validate: whether the AI actually understood the user and gave a good answer.
The AI remembers. Every message. 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, but throughout the entire interaction.
Facts, not fabrications. 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.
Real users don’t write clean prompts. We test how the AI handles conflicting instructions, off–topic requests, typos, multi–language input, emotional messages, and deliberate attempts to break the bot – because that’s exactly what production traffic looks like.
One bad response can go viral. We verify that the AI refuses harmful requests, protects sensitive data, avoids giving advice it shouldn’t, and resists prompt injection attacks – before the product reaches real users.
Structure matters downstream. We confirm the AI returns results in the correct format – valid JSON, proper markdown, appropriate length, right tone of voice, agreed output structure – so nothing breaks in the systems or interfaces that consume it.
Changes break things silently. When the model, system prompt, or retrieval logic is updated, we retest affected scenarios to confirm previously stable behaviour hasn’t shifted – because even a minor prompt change can alter how the AI handles edge cases or safety boundaries.
Technology Stack
Web:
Playwright
Cypress
Selenium
Webdriver IO
Serenity
Cucumber
Robot Framework
CI/CD:
Jenkins
GitHub Actions
Azure Pipelines
TeamCity
GitLab CI
CircleCI
Travis CI
Mobile:
Appium
Desktop:
WinAppDriver
Pywinauto
Performance:
Grafana K6
Jmeter
AI Product Testing Process
AI testing is part of your existing QA flow – not a separate project. Whether you're running manual or automated testing, AI coverage is added as a dedicated layer within the same engagement. Here's how it works.
STEP 1. DISCOVERY CALL
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.
STEP 2. AI QA ASSESSMENT
QA 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 the testing scope with your release timeline.
STEP 3. TEST PLAN & SCENARIO DESIGN
Engineers design the specific prompts, inputs, and scenarios that will be used during testing – standard use cases, edge cases, adversarial inputs, and scenarios targeting known LLM failure modes: hallucination, context loss, safety bypasses, and output formatting errors. Evaluation criteria are defined so that results are objective and reproducible.
STEP 4. EXECUTION & REPORTING
Engineers run the AI product through all defined scenarios, evaluating outputs against the agreed criteria. Every finding is documented with the exact input, the actual output, the expected output, and an assessment of severity and business impact. Findings are shared in a format that makes them immediately actionable.
STEP 5. STABILIZATION & DELIVERY
After your team addresses reported findings, QA engineers retest affected scenarios and run regression checks across the broader suite. Because AI behaviour can shift when prompts, models, or system instructions are updated, regression coverage is especially important here. All test documentation, scenario libraries, and findings are handed over to your team.
Why is AI Product Testing a Strategic Investment?
Standard testing cannot validate what AI actually does. Functional testing confirms the system works. AI product testing confirms it works well – whether the AI understands users, gives accurate answers, behaves safely under pressure, and produces output that holds up in the real world. For any product with an AI component, this is the difference between shipping something that performs and shipping something that looks like it performs until a user finds out otherwise.
No Hidden Failures
Catch hallucinations, safety gaps, and edge case breakdowns before they reach users – not after.
Reputational Risk Reduction
Unsafe outputs and data leaks carry business consequences. Finding them before release is significantly less costly than managing them after.
Full UX Validation
An AI feature exists to serve a user goal. Testing it means evaluating whether it actually does that – not just whether the API responded.
Non–Deterministic Coverage
AI outputs vary. QA engineers design scenarios that probe behaviour across a range of inputs and conditions to build a reliable picture of how the system performs.
Responsible AI Deployment
Regulators, enterprise buyers, and end users increasingly expect AI products to meet safety and transparency standards. Structured testing provides the evidence that due diligence was done.
Integrates with Your QA Process
AI product testing doesn't replace functional or automated testing – it extends it. QA Madness engineers add AI coverage without disrupting your broader release process.
Faster Go/No–Go Decisions
When your team has clear findings and severity assessments, they ship with confidence instead of hesitation.
Lower Cost of Quality
Fix AI failures at test time, not in production. The earlier a failure is found, the cheaper it is to resolve.
Built–In Expertise
We build our own AI products internally. We test AI from both sides – which means we know exactly where the risks hide. A missed AI bug doesn't just affect the user experience – it can cost you reputation and clients.
Success Stories
& Clients
“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.”
Marc Uitterhoeve
CEO at Dexter Agency
“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%.”
Alessandro Ronchi
COO at Bitbull Srl
“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.”
Alex Mathias
VP at Isadora Agency
FAQ
QA Madness AI product testing engineers answer the most common questions about testing AI–powered software – from what makes it different from standard QA, to how hallucinations are caught, what safety testing covers, and how to get started.
What makes AI product testing different from standard software testing?
Standard software testing validates deterministic behaviour – the same input produces the same output, and pass/fail criteria are clear. AI product testing evaluates non–deterministic outputs where the same input can produce different responses, and quality is often a matter of judgment rather than a binary check. It requires engineers who can assess whether an AI response is accurate, contextually appropriate, safe, and useful – not just whether an API returned a 200 OK.
What is hallucination testing, and why does it matter?
Hallucination testing evaluates whether an AI system generates false information with apparent confidence – inventing product details, prices, policies, quotes, or facts that don't exist. For customer–facing AI, hallucinations carry direct reputational and legal risk. QA Madness engineers design specific test scenarios to probe hallucination risk across the areas most relevant to your product and use case.
What does AI safety testing cover?
AI safety testing evaluates whether the system refuses harmful requests, avoids generating inappropriate content, protects sensitive data, and resists prompt injection attacks. The scope depends on your product's risk profile, but safety testing is recommended for any AI feature that interacts with end users.
Can you test AI products built on third–party models like ChatGPT or Claude?
Yes. Most AI product testing engagements involve products built on top of third–party LLMs via API. The testing focuses on the integration layer – how your system prompt, retrieval logic, and product context interact with the underlying model. This is where most product–specific issues occur.
How do you evaluate AI output quality when there's no single correct answer?
QA engineers define evaluation criteria during the assessment phase, in collaboration with your team. These criteria reflect your product's intended behaviour, brand tone, and user expectations. Where outputs require judgment, engineers assess them against these agreed standards and document their reasoning – making findings reproducible and giving your team a clear basis for prioritisation.
How quickly can AI product testing start?
QA Madness can begin within one to three days of project kickoff. The assessment phase typically takes two to five days, depending on the complexity of the AI feature and the availability of documentation. Test execution begins as soon as scenarios are designed and the testing environment is accessible.
Want to learn how to add AI testing to your QA process? Book a call with our team.
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