AI content review workflow

A stable, automated workflow that routes content through an LLM scoring step before publishing. The LLM evaluates each piece against defined criteria and returns a score, giving the system a consistent signal to act on — approve, flag, or reject — without a human in the loop for every item.

Automating review with an LLM trades some scoring nuance for consistency and speed. A human reviewer might catch edge cases the model misses, but a human also introduces variance across items and time. This workflow accepts that tradeoff deliberately — the LLM applies the same criteria every time, which matters more at volume than perfection on any single item.

Status is marked stable — the workflow runs in production without manual intervention per content item. The state machine structure (draft/review/apply/reject) means failures surface at a known step rather than silently corrupting output. That's the verifiable outcome: a repeatable, inspectable pipeline that doesn't require babysitting.