LLM Write API
Connect language-model output to a content workflow without losing the review boundary.
An LLM writing integration needs a contract between the application and the model. The application supplies an assignment and context; the model returns a draft or structured result. Your own validation and editorial process decide whether that result is usable. Do not make the model responsible for silently defining its own acceptance criteria.
Choose the result your application needs
Start with a title, outline, body, and separate review notes if those fields match the next step. Validate required fields and handle incomplete results explicitly. Model-specific request syntax belongs in an adapter so the editorial record does not have to change every time you evaluate a different configuration.
Compare complete editorial tasks
Use the same briefs and review rubric for each candidate. Assess support for claims, completeness, formatting, uncertainty, and editing effort separately. Include a case with insufficient evidence. Do not pick a model solely because its strongest sample sounds impressive or its individual request appears inexpensive.
It identifies the model configuration used, preserves the input brief, rejects unusable output cleanly, and never equates generation success with approval. Keep a repeatable evaluation set for changes to prompts, providers, and formats.
Take the next step
For a worked editorial approach, read How to choose an LLM for your content workflow. Then explore AI LLM Model Write API for the connected part of the workflow.