
Structured AI decisions vs free text in automation
When a model chooses a route, a small set of allowed answers is easier to use than a paragraph. Generated prose is useful for explanations, but it creates unnecessary interpretation work when software only needs a category.
Separate the decision from the explanation
For a customer request, define categories such as billing, technical, account, and review. Then let a separate field explain the choice. Do not route by searching that explanation for words: a sentence saying a problem is not billing still contains billing. A bounded output avoids that parsing mistake, but it does not establish that the classification is correct.
- 1Define mutually understandable categories with examplesDefine mutually understandable categories with examples.
- 2Include an explicit review path for cases…Include an explicit review path for cases outside the taxonomy.
- 3Measure category errors on labeled records rather…Measure category errors on labeled records rather than judging prose quality.
Try it on a small example
- Define mutually understandable categories with examples.
- Include an explicit review path for cases outside the taxonomy.
- Measure category errors on labeled records rather than judging prose quality.
What to verify
Watch categories with overlapping definitions. If technical and account both include login problems, resolve the policy before adjusting the prompt. Baleybots' pipeline composer uses closed-set decisions for structural choices and a separate writer contract for words. The broader design lesson is to ask the model only for the uncertain decision, while code assembles and validates the surrounding structure.
Product details checked against the Pipeline architecture on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.