“Looks great” feels encouraging but rarely changes a product decision. “I would add dark mode” is more specific, yet it still hides the reason, context, and priority. The problem is not that users are bad at feedback. It is that broad prompts produce broad opinions.

A better process starts with a decision. Do you need to learn whether the first screen is understandable, whether the core workflow produces a valuable result, why people abandon a step, or whether someone would commit time or money? The answer determines whom to ask, what task to observe, and which follow-up questions matter.

Actionable feedback has four parts:

The decision belongs to the owner because one comment is one piece of evidence. It may reveal a severe blocker, a niche preference, a wording problem, an unmet need, or a symptom whose cause is elsewhere.

Ask about specific, recent behavior before asking for an opinion. People can describe what they just tried more reliably than they can predict how they will behave in an imagined future.

Use prompts such as:

Avoid leading questions such as “Was the dashboard easy to use?” They suggest the desired answer and compress a complex experience into yes or no. Ask “What do you think you can do from this page?” and watch the first action.

The GOV.UK Service Manual recommends planning research around clear objectives and choosing participants who represent the users of the service. That principle scales down well for an early AI-built product: define what you need to learn and recruit people who actually face the problem.

You do not need a formal laboratory. A focused 20–30 minute session can produce useful evidence if you separate the task from the interview.

Start by saying that you are testing the product, not the person. Give a task with a goal, not click-by-click instructions. If they get stuck, wait long enough to observe what they try. Rescue the session only after you have captured the friction.

When recording, keep facts and interpretations in different columns. “Clicked Pricing, then returned to Home” is an observation. “Did not trust the price” is an interpretation unless the participant said it and explained why.

A public project comment can use a compact version of the same method:

For example: “While trying to share the generated plan on mobile, I tapped the copy icon twice because there was no visible confirmation. I expected a short success message, so I was unsure whether the link had copied. A temporary ‘Copied’ label could make the state clear.”

The example does not claim that the proposed UI change is the only solution. It gives the builder the evidence needed to reproduce the moment and choose a response.

Do not place credentials, personal data, private customer details, security vulnerabilities, or non-public links in public feedback. Contact the owner privately when the finding could put users at risk.

Group notes by the user job and the observed obstacle, not by the proposed feature. Five people may request five different solutions to the same underlying problem. Conversely, identical feature requests may come from unrelated needs.

Score each theme using evidence that fits your stage:

| Question | Stronger evidence | Weaker evidence | | --- | --- | --- | | Does it block the core job? | User cannot complete the intended task | General preference | | How often does it occur? | Repeated behavior or product data | One unprompted prediction | | Who is affected? | Intended user in a real context | Friend outside the target group | | What is the consequence? | Lost result, trust, time, or commitment | Cosmetic disagreement | | Can we test a response? | Small reversible change with a success signal | Large redesign with no measure |

Urgent security, privacy, data-loss, accessibility, or payment failures should not wait for a popularity score. Route them to the appropriate private or operational process.

Every meaningful theme needs an explicit disposition: investigate, accept, defer, decline, or resolve. Add the reason and the evidence that would change the decision. This closes the loop for contributors and prevents the backlog from becoming a list of unowned opinions.

A good response might say: “Accepted for investigation. We reproduced the missing confirmation on iOS Safari. We will test a visible success state this week.” A valid decline might say: “Not planned. The requested export format belongs to a different workflow than the one this project currently serves.”

Heyviber's project publishing flow is designed to keep the requested feedback and owner decision attached to the project context. Review the contribution guidelines before posting public suggestions.

We translated public GOV.UK user-research guidance into a lightweight loop for independent builders: define the objective, recruit relevant users, observe realistic tasks, separate facts from interpretation, and connect findings to a product decision. The example notes are original teaching examples, not quotes from real users.

The examples in this article are teaching examples, not testimonials or measured outcomes. When publishing real feedback, use permissioned or appropriately anonymized notes and preserve the context that makes the evidence useful.

Choose one product decision and invite three relevant people to complete the same realistic task. Capture where their behavior converges and where context explains the differences. Then write one owner decision, even if the decision is to collect more evidence.

That small loop is more valuable than a large survey full of vague satisfaction scores. It changes feedback from an inbox into a disciplined way of learning.