Testing AI-powered products as whole systems, not just the model
An AI feature is a model, prompts, data, integrations and an interface working together. We test how they behave as a product: whether answers are right, safe and consistent for real users, and whether everything around the model fails gracefully.
Overview
What it is. AI application testing checks products where a model produces or influences what users see or do: assistants, search and recommendations, summarisation, classification and workflow automation. We test the full path from user input to final outcome, including the prompts, retrieval, tools, guardrails and interface around the model.
Who it is for. Teams shipping their first AI feature, products adding AI to an existing workflow, and organisations that depend on a third-party model and need to know how their product behaves when it changes.
What you get. A risk-ranked view of how the AI behaves in your product, the evidence behind each finding, the limits of what was tested, and checks you can re-run when the model, a prompt or the data changes.
Why it matters
The problem it addresses. AI output is probabilistic: the same input can produce different answers, and a change to a prompt, model version or data source can shift behaviour with no code change. Conventional pass-or-fail checks cover only part of that.
When it matters most. Before launch, when changing models or prompts, when adding data sources, and whenever AI output feeds a decision or triggers an action.
What happens without it. Wrong or unsafe answers are found by users. Behaviour drifts after a silent model update. Teams cannot say what was tested, or what is still risky.
What we test
Output quality
accuracy, relevance and completeness of answers, checked against agreed reference cases and expert judgement
Consistency and robustness
how outputs vary across repeated, reworded, noisy and ambiguous inputs
Prompts and guardrails
system prompts, instructions and safety rules, including attempts to override them
Retrieval and grounding
whether answers are supported by the sources the product provides, and what happens when sources are missing
Integrations and tool use
calls from the AI layer to APIs, databases and other tools, including failure paths
User experience and fallback
how the interface handles slow, empty, wrong or refused answers
Change regression
a fixed evaluation set re-run after any model, prompt or data change
How UpDoer approaches AI Application Testing
Understand
Requirements, tickets, code diffs and product context.
We gather the product spec, prompts, model and data sources, user journeys and known failure reports, and agree what a good and a harmful answer look like for your users.
Risk Mapping
Where a failure would cost the most.
Journeys are ranked by what a wrong answer would cost, with actions the AI can trigger, advice users act on and personal data first.
Test Strategy
Coverage chosen, test cases drafted.
We choose reference cases, input variations and adversarial probes, and decide what runs automatically on every change and what needs a person each time.
Execute
Manual and automated testing runs.
Reference cases and probes run against agreed environments. Exploratory sessions try the unexpected: odd phrasing, long conversations, missing data and conflicting instructions.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps draft input variations, group similar failures and give each output a first-pass score against a rubric, so triage starts from patterns instead of a long list.
Human Verification
A QA engineer checks every flagged result.
A QA engineer reads the outputs behind every flagged result, separates real failures from normal variability, and confirms severity and reproducibility.
Report & Improve
The verified quality signal, then better coverage.
Findings are reported by risk with known gaps stated plainly. Confirmed failures become part of the evaluation set that is re-run when the model, a prompt or the data changes.
AI Application Testing: from output risk to release signal
01
Where it breaks
Wrong answersSilent model changesUnsafe replies02
Test workflow
Reference casesVariationsGuardrailsRegression03
AI assists
Draft input variantsGroup similar failuresFirst-pass scoring04
Humans decide
Judge what is correctOwn the release call05
Release signal
Coverage by riskOpen findingsGo / no-go by a person
Illustrative workflow. Not a client result or a measured outcome.
AI application coverage matrix
| Quality area | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Output quality | Accuracy, relevance and completeness against reference cases | Drafts variants of each reference case | Judges what counts as correct for the domain |
| Robustness | Rewording, typos, long or ambiguous input | Generates input variations | Decides which variations matter to real users |
| Safety and guardrails | Refusals, instruction overrides, out-of-scope requests | Proposes adversarial prompts to try | Assesses real-world risk and severity |
| Grounding | Answers supported by the provided sources | Flags statements with no visible source | Confirms that a source really supports the claim |
| Integrations | Tool calls, data access, timeouts and failure paths | Suggests failure scenarios | Verifies that the resulting action is correct |
| Change impact | Behaviour after a model, prompt or data change | Re-runs and compares evaluation sets | Decides whether a shift is acceptable |
Quality area: Output quality
- WHAT WE VALIDATE
- Accuracy, relevance and completeness against reference cases
- AI ASSISTANCE
- Drafts variants of each reference case
- HUMAN VALIDATION
- Judges what counts as correct for the domain
Quality area: Robustness
- WHAT WE VALIDATE
- Rewording, typos, long or ambiguous input
- AI ASSISTANCE
- Generates input variations
- HUMAN VALIDATION
- Decides which variations matter to real users
Quality area: Safety and guardrails
- WHAT WE VALIDATE
- Refusals, instruction overrides, out-of-scope requests
- AI ASSISTANCE
- Proposes adversarial prompts to try
- HUMAN VALIDATION
- Assesses real-world risk and severity
Quality area: Grounding
- WHAT WE VALIDATE
- Answers supported by the provided sources
- AI ASSISTANCE
- Flags statements with no visible source
- HUMAN VALIDATION
- Confirms that a source really supports the claim
Quality area: Integrations
- WHAT WE VALIDATE
- Tool calls, data access, timeouts and failure paths
- AI ASSISTANCE
- Suggests failure scenarios
- HUMAN VALIDATION
- Verifies that the resulting action is correct
Quality area: Change impact
- WHAT WE VALIDATE
- Behaviour after a model, prompt or data change
- AI ASSISTANCE
- Re-runs and compares evaluation sets
- HUMAN VALIDATION
- Decides whether a shift is acceptable
Where AI helps
- Drafting varied test inputs, paraphrases and edge cases from a specification, as a starting point for review.
- Grouping similar failures by prompt pattern, topic or data source so one cause is not triaged many times.
- Giving candidate outputs a first-pass score against an agreed rubric, which a person then checks before anything is reported.
- Summarising a run into a readable risk note that a QA engineer confirms or corrects.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Deciding what a correct, acceptable answer means in your domain, which no automated score can settle.
- Judging tone, safety and potential harm in context.
- Confirming that a flagged failure is real and not noise from normal variability.
- Setting the thresholds and owning the release recommendation. An AI score is an input; a person signs off.
- Stating the limits of what was covered so nobody mistakes testing for assurance.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
How do you test something that gives different answers each time?
We stop treating a single run as the result. Reference cases are run several times and with variations, outputs are judged against an agreed rubric rather than exact text, and a QA engineer separates normal variability from a real failure.
Do you test the model itself or the product around it?
The product around it. We test how the model, prompts, data, tools and interface behave together for your users. If you need to evaluate a model in isolation, we can agree what that covers and how its limits will be reported.
Can you test a product built on a third-party model?
Yes. Because the model can change without your code changing, we build an evaluation set you can re-run whenever the provider updates it, so you can see what moved.
Will you tell us the AI is safe?
No testing can promise that. We report what was tested, the evidence for each finding and what was not covered, so your team can decide whether the remaining risk is acceptable.