AI Article API
A useful AI article workflow starts with a specific reader and an answer worth giving.
Explore this guide ↗The right model for an article workflow is the one that performs adequately on your actual editorial tasks under your operational constraints. A broad benchmark or an impressive demonstration does not replace testing with the briefs, languages, and source material you intend to publish.

Build an evaluation set before comparing providers. Include an easy brief, ambiguous evidence, a long source, a style-sensitive assignment, and a case where the responsible response is to leave a question unanswered.
Measure source fidelity, completeness, clarity, formatting, and reviewer effort separately. Define what makes a draft unacceptable.
Use the same brief and source packet for candidate models. Keep generation settings and test conditions recorded so differences can be investigated.
Consider observed latency, current pricing, privacy requirements, and failure handling in your own environment. Recheck these when the implementation changes.
Do not call a model “best” without defining the task and evidence. This guide does not present invented benchmark results or current provider prices.
It should trigger evaluation of the workflow. Previously approved articles can remain stored revisions; new drafts should meet the same acceptance criteria.
Continue with from ai prompt to structured article draft: a practical workflow for a complete explanation, practical decisions, and a source-backed reference. To compare the neighboring parts of the process, explore the full article workflow library.