# Review an AI summary as claims, not just prose > Source: > Published: 2026-09-28 02:49:08+00:00 *Disclosure: This article was written by AI. Automated checks are not independent fact verification. This is source-based analysis, not a hands-on product test.* An AI summary can be readable and still be unusable. A product announcement becomes a proven result; a future rollout becomes something available today; a number loses the condition that made it meaningful. Proofreading the sentences does not necessarily reveal those changes. For an automated newsletter or technical blog, consider treating the summary as a set of claims to check, rather than a paragraph to polish. The procedure below is a proposed editorial workflow. It does not promise error-free writing. Before asking for prose, ask the model to list the statements it intends to use. For each statement, request a short supporting passage and identify who made the claim. Keep that evidence in an internal review record rather than reproducing long quotations in the published article. Then check the passage against the original document. A passage that really appears in a source can still be attached to a claim it does not support. Matching text is useful for detecting fabricated quotations, but it is not a substitute for reading the surrounding context. For example, a statement about a company's internal evaluation should remain attributed to that company. It should not become a recommendation based on independent testing. Give the draft separate places for source-backed facts, your proposed interpretation, and open questions. This makes review more concrete than asking whether the whole article sounds plausible. Suppose a source announces a new collaboration tool. The announcement belongs in the factual section. A suggestion to evaluate it during design reviews belongs in the proposed-method section. Whether it handles your team's accessibility requirements belongs in the questions section unless evidence answers it. Do not move an unanswered question into the factual section just because the model offers a confident sentence. Remove the sentence or obtain a source that actually supports it. Extract every date, quantity, percentage, and price from the draft. Compare each one with its source and preserve the relevant conditions. Distinguish an announcement date from a release date, and a planned feature from one that is available. This is a review task, not a reason to add more numbers. If a figure is unnecessary to the reader's decision, leaving it out can make the article clearer. If it is central but insufficiently supported, hold the article rather than quietly treating it as approximate. Keep a draft's creation status separate from its approval status. After a revision, repeat the checks on the revised text. An approval attached to an earlier version should not silently authorize a different manuscript. The linked Next Question implementation illustrates exact-body review hashes, checks for supporting source passages, and publication holds. These are safeguards in an application, not proof that a manuscript is true. The repository also documents limitations of automated editorial checks. This procedure has no measured accuracy improvement attached to it. It can still miss misleading context, omissions, or errors in the source itself. For consequential claims, obtain stronger evidence and appropriate review before publication. The useful outcome is a visible decision trail: what was checked, what remains uncertain, and why the draft was released or held. [Next Question: editorial checks and exact-body publication controls](https://github.com/TaeHyun952/nextquestion/tree/60e2ae9f0071b61ac2ebb26da558436af3869421/src)