A media hiring workflow should select a model for text summarization through a Node.js API by testing the same candidate packets, then promote the winner behind one stable adapter. For basic rubric extraction, start with a lightweight catalog option such as deepseek-v4-flash-0731 ; for a nuanced candidate summary that must explain trade-offs, test an advanced option such as glm-5.1 . The important choice is not permanent brand loyalty. It is how quickly the team can compare providers without rewriting the review service.

1. What does a useful first result require?

The first useful result is a structured summary of one candidate against a known rubric: evidence, missing evidence, and a short recommendation. That is a narrower target than “summarize this document.” It gives us a repeatable sample and exposes integration cost early.

I would use five candidate packets: a short resume, a long portfolio, a transcript, a sparse application, and one deliberately ambiguous packet. Keep the rubric fixed.…