🧠 AI & writing · Topic field guide

LLM Article API

A language model can return fluent prose while missing the structure your publishing system expects. An LLM Article API pattern makes that interface explicit: what goes in, what comes out, and which outcomes need a person to intervene. The goal is a draft you can inspect, not a response you merely hope will work.

LLM Article API — bright rainbow typography and 🧠 emoji

Start with the right boundary

Keep the provider adapter separate from the editorial content model. That makes it easier to compare candidate models using the same briefs and acceptance criteria without redesigning every publishing destination.

A practical workflow

01. Define the output shape

Choose a small schema for the title, sections, summary, and unresolved facts. Avoid asking one response to generate an entire publishing operation.

02. Handle non-success outcomes

Plan for refused requests, incomplete output, connection failures, and responses that pass structural checks but fail editorial review.

03. Evaluate representative work

Compare drafts on the actual languages, formats, and source complexity your team publishes. Record reviewer changes rather than relying on a generic quality score.

Before the next handoff

  • Provider-specific fields stay out of the article record.
  • Error and review states are visible.
  • Model changes have a repeatable test set.
KEEP THIS IN MIND

Do not promise identical prose across model changes. Preserve the accepted article revision instead of regenerating it every time a reader requests the page.

Does a schema guarantee a factually correct article?

No. A schema can describe acceptable structure. Evidence review and subject-matter judgment are separate requirements.

Take the next step

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.