Start with a bounded assistive feature. Define useful output, evaluate failure cases, and keep a clear fallback inside the workflow.
Choose a task with an observable result
“Add AI” leaves both the product and the evaluation undefined. A more useful brief describes an action: prepare a support summary, suggest a category, or help a user locate a relevant document. Decide what a successful result looks like before choosing a model or building an interface.
For a support summary, usefulness might mean preserving the customer’s issue, attempted fixes, and outstanding questions without adding facts. A reviewer should be able to compare the draft with the original conversation. This is a narrower and more testable feature than an assistant that promises to handle every support task.
Match the feature to the cost of being wrong
The same model output can be acceptable as a draft and unacceptable as an automatic action. These are product design choices to make explicitly.
Swipe the table to compare all columns →
| Feature | Useful output | Review or fallback |
|---|---|---|
| Summarization | A shorter view of source material | Open the source and edit the draft |
| Classification | A suggested label or queue | Correct the label or leave it unassigned |
| Document search | Relevant passages with references | Show no useful match when evidence is missing |
| Drafting | An editable starting point | Require review before sending |
| Recommendations | A ranked set of options | Offer ordinary browse and filter controls |
Keep evidence and permissions in the workflow
If a feature uses private documents, enforce access before selecting the material supplied to the model. A generated answer is not a substitute for the application’s authorization rules. Treat retrieved text as data, including text that appears to instruct the model to ignore its task.
Show enough evidence for the user to judge an answer. A link to the source passage is more useful than a confident tone. Provide an ordinary way to continue when generation is unavailable or the source material does not support an answer. For consequential actions, keep explicit approval in the interface.
- Authorized context
- Draft or retrieve
- Check the evidence
- User decides
Create a small evaluation set
Collect representative examples and write down what a useful answer must preserve. Include ambiguous inputs, missing information, conflicting sources, and attempts to redirect the task. Keep a set of examples separate from those used while tuning the feature so you can detect changes that only improve familiar cases.
Review correctness, usefulness, latency, and cost together. A response can be accurate but too slow to fit the task. Track model and prompt changes so unexpected behavior can be traced to a release. Avoid publishing an accuracy percentage unless the task, sample, and evaluation method are clearly defined.
Expand only after the assistive version helps
Observe whether users keep, edit, or discard the output. Ask what verification they still need to do. Frequent corrections can reveal missing context or a task that should stay rule-based. Review failure examples instead of treating every use as evidence of value.
A small feature with a clear boundary is easier to support and improve. Broader automation can follow when the team understands the error modes and the users trust the workflow for good reasons.
