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Find a specific task worth improving.
Useful starting points often involve repeated work: summarising incoming requests, suggesting a category, or helping someone draft a response. Describe the existing workflow before choosing a model.
Compare an AI approach with simpler alternatives. Rules, better search, or a clearer form may solve the problem with less complexity.
Make the boundaries visible.
Decide what information the feature needs, what it is allowed to do, and which actions require a person to approve them. An assistant that prepares a suggestion has different responsibilities from a system that executes it.
Keep data access limited to the task. Give people an understandable explanation of how their input is used and what they should review.
- Limit inputs to the information the task needs.
- Require review for consequential actions.
- Provide a clear fallback when the feature cannot help.
Evaluate on realistic examples.
Test representative cases, including incomplete inputs and unusual requests. Agree what makes an answer useful and what kinds of mistakes are unacceptable for the task.
Keep an eye on response time and ongoing cost alongside output quality. A technically impressive result still needs to fit the experience and the operating budget.
Keep human control in the design.
Show suggestions as suggestions. Make it easy to edit, reject, or try again, and avoid hiding uncertainty behind confident interface language.
At Raketa Labs, AI work starts with the product problem and the workflow around it. The model is one part of the solution, alongside integration, evaluation, and interface design.