AI wrote the first draft. What's still up to me?

Suppose AI helps you summarize an article

Imagine a workflow that reads source material, creates a summary, and turns it into a post. Every step could return success while the result still contains inaccurate summaries, unsupported claims, or a draft that needs substantial rewriting.

I'd separate two questions: did the system work as intended, and is the final content useful? This article proposes a method; it is not a report of measured results from an existing project.

What makes a draft usable?

Start with a specific task to make quality easier to judge. For example, summarizing a sourced article gives us something concrete to review using the following criteria.

Criterion	Review question	If it falls short
Faithful to the source	Can each important claim be traced to the original text?	Flag the claim and return it for revision
Context preserved	Are any conditions or limitations missing?	Restore the relevant context
Useful to the reader	Can the reader tell when this applies?	Clarify the use case

Track your own editing time

A quick generation step may offer less benefit than expected if every draft takes a long time to edit. Keep the original process as a baseline and include review, revision, and exception handling in the measurements.

Count all of it, from gathering the material to the final edit.

If the draft isn't ready, send it back

A few simple rules make it clear what should happen when a problem appears. The example below illustrates a data structure; it is not a complete, executable review system.

const review = {
  task: "summarize_a_document",
  checks: ["source_accuracy", "missing_context"],
  humanReview: "required",
  onFailure: "return_to_draft"
};

Returning a piece to draft, keeping its sources, and allowing human edits is often easier to understand and manage than unlimited retries. The right approach still depends on the task and its risks.

A place to start

Choose a recurring task with a clear scope.
Prepare comparable inputs and document the original process.
Write down the criteria for an acceptable result.
Record human edits and how failures are handled.
Use the results to decide whether to keep, change, or stop the workflow.