Node.js Text Classification: Implementing Structured Output with a GPT API

TL;DR: For a Node.js implementation of text classification using a GPT API, the important design choice is not which text model sounds best. It is whether the classifier returns a contract that the review queue can validate, retry, and safely apply twice. Put the model behind a narrow adapter, require a small structured result, reject malformed output, and make the write path idempotent.

A report classifier should be treated like an unreliable dependency in a production queue. The useful result is a label, a confidence value with a defined meaning, and enough trace context to explain why a human reviewer received the item. Everything else is optional prose.

Retries are part of the design.

Make the write conditional.

How should Node.js implement text classification using a GPT API?

Start with the workflow, not the prompt. A media report might arrive with a report ID, the reported text, a policy category, and a review priority.…