AI onboarding: give new users a first result they can actually check
Design a bounded first AI task with a known input, inspectable claims and a correction path, so users can judge the output before relying on it.
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The first generated answer is a weak definition of success for an AI product. A response can be fluent, quick and wrong about the detail the user needed. Onboarding should help the person judge a useful result, including the parts that require correction.
Start with a task whose input is understandable and whose output can be checked without specialist knowledge. For a document assistant, that might be extracting the agreed next steps from a short note, with links back to the relevant passages. The task should be small enough that the user can evaluate it, yet close enough to real work to explain why they would return.
The goal is a supported first decision. A decorative example prompt that produces an impressive paragraph may leave the user with less understanding of the product’s limits.
Give the starting point a complete task
“Summarize something” asks a new user to supply the document, invent the request and decide what a good answer looks like. A more useful starting point provides a short input, a specific question and a visible way to check the result.
For an illustrative document assistant, use this fictional project note:
The team agreed to publish the supplier guide on 18 May. Asha owns the final text. Marco proposed adding a comparison chart, but the team has not decided whether it belongs in the guide. The launch budget was not discussed.
The proposed starting task is: “List the agreed publication date, the person responsible for the text and any unresolved decision. If the note does not give a budget, say that it is not stated.”
This is an authored example, not output from a tested model. It gives the user a bounded job: distinguish an agreement from a proposal and avoid inventing missing information. Those are useful behaviors to inspect in a document product.
Do not describe the example as representative of the product’s accuracy. A short, clean note leaves out many difficulties in real documents, including contradictory sources, unclear pronouns and outdated revisions.
Define what an acceptable result would contain
For this example, an acceptable response would identify 18 May as the agreed publication date, Asha as the text owner, the comparison chart as unresolved and the budget as unstated. It should point each assertion to the relevant part of the note.
A deliberately flawed response might assign the chart to Marco as a confirmed task or present a budget estimate. Both could sound reasonable. Neither follows from the input. Showing this contrast during an internal design review makes the acceptance criterion more precise than asking whether the summary “looks good.”
The interface can keep the source beside the generated result, with short, inspectable claim units. A person should be able to check a date without reading a long explanation of how the system works. Where a claim depends on several passages, let the person inspect that context instead of showing a citation marker that leads only to the top of a document.
Google’s Gemini Notebook chat guidance documents this kind of navigation: users can inspect citations and select them to reach the quoted source context. That supports checking a response; it does not make every response correct by definition.
Put correction inside the useful journey
If the output overstates the comparison-chart proposal, the user needs a direct way to correct that item while keeping the rest of the work. A generic thumbs-down reaction may collect feedback, but it does not necessarily repair the note the person wants to use.
In our illustrative product, selecting the claim could offer “Mark as unresolved” and an editable text field. The corrected result should remain visibly different from a newly generated suggestion. If regeneration would replace the user’s edits, explain that consequence before proceeding.
Keep the scope of a correction clear. Editing this summary does not automatically mean the model has learned a lasting preference, changed the source document or updated every future answer. Tell the user which artifact changed.
A good first task can end with the corrected summary saved as a working note. That is a useful outcome even when the first generation needed revision. It also teaches a more sustainable habit than treating every generated sentence as ready to forward.
Make explanations useful without encouraging blind trust
Microsoft’s HAX guidance on explanations recommends giving users access to explanations of AI behavior while cautioning that the presence of an explanation can increase trust and encourage over-reliance. More explanation is therefore not automatically better onboarding.
For the fictional note task, the useful explanation is close to the claim: “This date comes from the team’s agreed publication sentence.” A long narrative about reasoning may make the answer feel authoritative while adding little to the user’s ability to check it.
Avoid a confidence percentage unless the product has a defined, meaningful basis for displaying it. A user needs to know whether the cited passage supports the claim, whether the relevant source was included and whether an unresolved point has been presented as settled. A decorative number cannot answer those questions.
When the product lacks enough information, give that state a useful continuation. “The budget is not stated in this note” can be followed by an option to add the relevant document or leave the field unresolved. Do not make the user keep prompting until the system supplies a more satisfying fiction.
Move from the example to a real document carefully
After the sample task, invite the user to bring a short document they understand and are permitted to use. Keep the same review method: identify the requested claims, inspect the supporting passages, correct an overstatement and save the result.
Explain what will happen to the supplied document using the product’s actual data controls and policies. Do not imply that a tutorial’s small sample establishes suitability for confidential or high-stakes work.
Preserve the distinction between source selection and output checking. If the user supplies an outdated document, a faithful summary can still be unhelpful. The onboarding interface should show which document or revision it used so the person can notice a mismatch before relying on the result.
The next task can be more complex once the user understands this relationship. Starting with a pile of unfamiliar documents makes it difficult to tell whether the failure lies in source selection, retrieval, interpretation or the requested output.
Observe whether the person can judge the result
For an internal usability review, ask participants to explain the sample answer and locate the support for one selected claim. Include the deliberately flawed comparison-chart item in a prototype to see whether the correction path is discoverable. This is a proposed design exercise, not an accuracy study or a report of tested participants.
Record what the person accepted, what they questioned and whether they could repair the output. Keep generation, inspection, correction and saving as separate observations. A completed generation event should not stand in for all of them.
The first useful result is one the user can account for: they know what it says, where the important claims came from and how to change what is wrong. That gives the next AI-assisted task a sounder starting point.
