AI Content Updates: Change Facts Without Losing Context

When an AI agent updates a fact, ask it to trace what depends on that fact. A corrected figure may change a chart, a comparison and the page’s opening summary. Replacing the first matching number can leave the article internally inconsistent even though the new number is accurate, and a reader who meets the old figure in the chart and the new one in the text will trust neither.

The right scope is the smallest complete factual correction. Preserve source qualifications and historical context, update every affected representation, then explain any change to the conclusion. Leave unrelated copy alone unless the assignment includes a broader edit. This playbook gives the agent a revision map to fill in before it edits, a way to keep the source attached to the claim, a worked example, the three dates that must not be merged, and the review that catches what a diff cannot show.

Two words carry the whole method. Smallest: the edit touches only what the new evidence changes, so the article keeps its date, its voice and every claim the evidence did not reach. Complete: it touches every place the changed fact appears or is relied on, so the article agrees with itself afterwards. The second is the harder half for an agent, because it requires reading the whole page with one question in mind rather than searching it for a string.

Why not simply rewrite the page with the new fact? Because a rewrite discards the evidence that the rest of the page was right. Every qualification an earlier editor added, every “as of” and “according to”, is a small verified claim, and a fluent rewrite will drop some of them without noticing. The correction should be a surgical event that the next editor can see and reverse, not a new draft.

Before editing, record the old assertion, the replacement evidence and every place that derives meaning from it. This proposed map keeps a factual update inspectable without turning it into a full rewrite. Each row is a place the fact can hide, the question to ask before touching it, and the action that keeps it consistent with the corrected source.

The rows are ordered from the obvious to the easily missed. The primary assertion is the easiest to find. A derived number has to be recomputed from its inputs rather than adjusted by eye. The searchable description is the easiest to forget, and an outdated description can keep showing in search results until the page is recrawled.

W3C’s PROV primer describes usage, derivation and revision relationships. Provenance means information about where an output came from and how it was produced. We use that idea to connect a source fact to the passages and visuals that depend on it: the sentence was derived from the source, the chart was derived from the sentence’s inputs, and the revision replaced one and therefore must revisit the other.

You do not need a formal provenance database for a small article. A short change record can identify the source location, the old claim, the replacement and the affected sections. Keep enough information for the next editor to understand why the change was made, because the next correction will arrive with its own new evidence and the editor will need to know which earlier claims that evidence supersedes.

Review source meaning as well as its number. A revised report may redefine the population or replace a preliminary estimate with a different measure, in which case the new figure is not a correction of the old one but a different fact that happens to share a label. The citation-check reference is useful before the agent begins changing text.

Imagine an illustrative article that describes a survey result as covering all businesses. The source actually covers only respondents with an existing AI project. The correction is not just a phrase in the results table. The headline summary and recommendation may also have overstated how widely the result applies, and the chart title probably says “all businesses” in a font nobody re-reads.

Preserve the reported value if it is otherwise correct, attach the population qualifier and re-evaluate the conclusion. If the article used that result to recommend immediate adoption to every reader, the recommendation now needs narrower support or a clear editorial rationale. The number did not change; what it was evidence for did, and that is the harder kind of correction because a string search finds nothing to replace.

Our editing recommendation: check the chart title, notes and legend as part of the correction. Bars can remain numerically accurate while a title such as “all businesses” still overstates the population. Walk the map row by row for this one claim and the edit touches five places, none of which contains the digits 4 and 2 in the wrong order.

Preserve the observation period and original publication date. Record the actual review date separately, with the scope of what was checked. Three dates, three facts: when the thing was true, when the article first said so, and when someone last looked. An article that shows only the newest of the three has told the reader that everything on the page is as fresh as the last edit, which a narrow correction cannot make true.

Avoid automatic “latest” language after a narrow edit. Fixing a broken source link does not establish that every specification or comparison on the page was rechecked. State the scope of the review in the change record. “Reviewed: the survey population claim and its dependents” is honest; “Reviewed” alone implies the whole page.

The same discipline applies in reverse. If the correction is that a figure was true in one year and the article now describes a later one, the old figure may belong in a dated historical sentence rather than in the bin. The map’s last row exists for exactly that case: a claim about then is not wrong because now is different.

Each illustrative failure below is the agent doing exactly what it was asked and the page being worse for it. The common thread is that the agent was given a string to change rather than a fact to trace.

The half-fix: the prose says the new figure, the chart still plots the old one, and the reader assumes the chart is right because charts look authoritative. The over-reach: asked to correct a price, the agent also “updates” three adjacent prices to what it believes are current values, none of which were in the brief and none of which it checked. The quiet promotion: a narrow edit resets the visible “last updated” date, and the whole page now claims a freshness only one sentence earned. The lost hedge: the source’s “preliminary” was attached to the old figure and does not survive the replacement, so a provisional number becomes a settled one.

The map is designed to catch all four, because it forces the agent to answer “where else does this fact live?” and “what did the old sentence say about the fact that the new one must still say?” before it edits anything.

Read the revised opening and conclusion without the body. They should still accurately describe what the evidence supports. Then inspect the table, chart and footnotes together, and open the consequential citations from the finished page or file. A diff shows what changed; it cannot show what should have changed and did not, which is the whole risk of a half-fix.

Ask a reviewer to check the revised claim against the source rather than simply approving the agent’s change summary. The guide to independent review checks explains how to avoid two reviews inheriting one mistaken interpretation. The same source-first habit protects translated versions of the page, where a corrected fact has to travel into every language; our translation QA playbook treats that as the same contract.

Finally, verify the delivered artifact. An exported copy may still contain an old chart even when the source document has been corrected. Apply the file acceptance checks to the actual handoff. Teams that want the revision map built into their content operations rather than remembered can work with our AI transformation practice.