The feature works. How do I make it easy to use?
Walk through a real task from input to waiting, result, and recovery, so someone else can complete it independently.
On this page
Direct answer
Calling an API is only one step. People also need to understand what the product does, what to enter, what is happening, whether the result is usable, and how to recover from failure. Put a working preview in front of someone, observe, and revise. Checking states along a complete task usually matters more than adding animation. Acceptance should cover functionality and practical use. Wanting to share the product is useful to observe, but does not replace correct inputs and outputs or recoverable errors.
When to use
- Your demonstration works, but other people get stuck.
- The page looks complete, but you do not know how to evaluate the experience.
- Slow model calls cause people to submit repeatedly or leave.
Step by step
Write one task that needs no explanation
Choose something the target user would actually do, such as turning meeting jargon into actions or comparing a garment preview. Record what they know before arriving and what they should have afterward. Organize entry points, button labels, and result explanations around that task.
Explain inputs and results
Provide an example to try and state allowed formats, lengths, or image requirements. Explain what the result establishes, what remains uncertain, and whether users can copy, download, or revise it. Internal model names, parameters, and logs should not become the main product explanation.
List the states at each stage
Cover initial, editing, processing, success, failure, and no-result states; upload tools may also need cancellation and reselection. Show truthful progress rather than an unsupported countdown. Handle repeated submissions so one action does not accidentally trigger several paid calls.
Resolve boundaries with explicit rules
For a game, specify rounds, score bounds, and termination order. For a tool, specify empty inputs, invalid files, and recovery after external failures. Remove contradictions in the written rules before asking AI to implement them.
Observe independent use and fix the important problem
Ask a user to perform the task without coaching. Record where they stop, what they misunderstand, and whether they believe the task is finished. Fix the obstacle that matters most, then check narrow screens, keyboard use, and failure cases. Convert feedback into changes rather than collecting only compliments.
Worked example
Hypothetical exercise: conflicting game rules
Suppose a game specifies both “minimum score -50” and “fail below -50.” If code clamps the score to -50, the second condition can never happen. Choose a consistent rule before implementation: fail on reaching the limit, or retain an out-of-range check without clamping first. A separate requirement to generate content dynamically with “no waiting” also needs correction. Provide honest loading feedback and measure latency; an external API cannot be promised to take no time.
Action checklist
Reusable templates
Core-flow acceptance card
Working template
User task: [desired outcome]
Starting point and input: [available information, allowed format]
Main steps: [action and expected feedback]
Evidence of success: [concrete result, beyond opening a page]
Failure cases: [empty input / timeout / insufficient permission / unusable result]
Recovery: [retry, edit, return, preserve completed work]
Observation: [obstacles, exact words, actual behavior]
Fix this round: [the single obstacle with the largest effect]
Pitfalls
- Animation rarely fixes not knowing what to do next. Resolve the flow first.
- Do not present fallback text after a network error as a normal AI result; explain that the task did not finish.
- Testing only familiar ideal inputs misses how newcomers actually behave.