Translating quality for non-testers
Have the model rewrite test findings and quality information for a specific audience — executives, product owners, customers or auditors — in their terms, at the right length.
How it works
The same finding needs different words for different readers. A developer needs the stack trace; a product owner needs to know which users are affected and how; an executive needs the business consequence and the decision required. The model is given the technical material and a description of the audience, and drafts a version for them: impact first, technical detail only where it changes the decision, and a clear ask. It can also do the reverse, turning a stakeholder's question into the specific information testing needs to provide.
When to use it
Reporting to management or clients; explaining a serious defect to people outside engineering; writing customer-facing notes on known issues; and testers who find this kind of writing slow.
Limitations
Simplification can change meaning: "a small number of users may see an error" is a different statement from "payments fail for customers with a saved card". Models soften findings by default and add reassurance nobody asked for. The model can't judge the politics of a message — what a particular executive needs to hear first, or what will be misread.
Watch for
A finding that needs a decision, ship with it or delay, comes back as “the team is investigating”, and nobody decides. Before sending, check that three things survived: who is affected, what fails, and what decision is needed.
What you still own
That the simplified version is still true, and how the message lands with the people receiving it.
Cite this
Qlarify Labs. (2026). Translating quality for non-testers. Retrieved from https://labs.qlarify.fi/ai-in-testing/translating-quality-for-non-testers