LLM Article API
A language model can return fluent prose while missing the structure your publishing system expects.
Explore this guide ↗A useful AI article workflow starts with a specific reader and an answer worth giving. The model can propose an outline, develop a draft, or suggest alternatives, but the brief and the publication decision still need clear owners. Treat generation as one stage in an editorial process rather than a shortcut around it.

Design a bounded assignment: approved sources, an intended audience, the questions to answer, a target structure, and material the draft must not invent. Keep observations from source documents distinct from instructions given to the model.
Provide the central question, reader context, approved evidence, and desired next action. Mark unknowns rather than inviting the model to guess.
Ask for separate fields for headings, body sections, source references, and unresolved questions. Route incomplete responses into review rather than silently filling gaps.
Check claims against the actual sources, inspect the argument, and edit for voice. Re-run checks when a model or prompt changes.
A confident tone is not evidence. Do not turn a model-generated reference into a citation without opening and checking the original source.
This is a guide library, not a hosted writing application. Use the examples to design your own workflow with a provider and publishing system you choose.
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.