Metadata is the descriptive information about a piece of content rather than the content itself: what this page is about, what this image shows, who wrote this document and when. It is the least glamorous part of content management and the part most reliably left undone, because writing it is tedious, repetitive, and produces no visible reward for the person doing it.
That combination — high volume, low enthusiasm, immediately checkable output — makes it one of the strongest applications of AI in a content platform. It is also one where the failure mode is specific and worth designing against from the start.
A note on scope. This article is about generating descriptive values: writing text that did not previously exist. Choosing which category or label a piece of content belongs to is a different operation with different risks, and it is covered in AI content classification.
Why metadata is worth the effort
Four things depend on it, and all four degrade quietly when it is missing:
- Search. Both your internal search and external engines use descriptive metadata to understand what content covers. Retrieval quality has a ceiling set by how well content describes itself.
- Accessibility. Image alt text is not optional. Missing or unhelpful alt text excludes people using screen readers, and it is one of the most common accessibility failures in large libraries.
- Reuse. Content cannot be surfaced in another channel if nothing describes it well enough to select it.
- Governance. You cannot review, expire, or audit what you cannot describe and find.
What AI can draft
Meta descriptions and page titles
Summarising a page for a search result within a length limit. Well suited to AI: the source material is right there, the constraints are mechanical, and the output is checkable in seconds. A human should still confirm that the emphasis matches what the business wants that page to be found for.
Image alt text
Describing what an image shows. Genuinely useful at scale — and the place to be most careful. A model describes what it can see; it cannot know that the photograph is of your Houston office rather than a generic workspace, or that the person shown is a named client. It will produce a fluent, confident description either way.
Two rules hold up: never let generated alt text assert an identity, place, or count the model cannot verify, and treat decorative images as decorative (empty alt) rather than describing them for the sake of a filled field.
Summaries and standfirsts
Condensing an article into a sentence or paragraph. Strong fit, because it is derivation from a source rather than invention. The main risk is a summary that reads well but promises something the article does not deliver.
Captions and transcripts
Video and audio transcription is mature and generally reliable, and it makes previously unsearchable content findable. Domain vocabulary — product names, technical terms, people's names — is where accuracy drops, so a glossary or vocabulary hint materially improves results.
Document properties
Title, author, date, and abstract for documents that were uploaded years ago with none of them. Often the fastest route to making a neglected document library searchable at all.
Fabrication is the failure mode
The characteristic risk of generation is not that the output is obviously wrong. It is that the output is fluent, specific, and unsupported — alt text describing a detail that is not in the image, a summary asserting a benefit the page never claims, a document abstract citing a figure that does not appear in the document.
This is harder to catch than a bad taxonomy term, because well-written text reads as authoritative. Three design choices reduce it substantially:
- Ground it and say so. Instruct the system to describe only what is present in the source, and to produce a shorter, plainer value when the source is thin rather than filling the gap.
- Prefer under-specification to invention. "Two people working at a shared desk" is more useful than a confident but wrong "the LABUSA delivery team in Houston". Where a count or identity is uncertain, describe the activity instead.
- Constrain length. Generated text expands to fill whatever space it is given, and the surplus is where invention appears.
This is not hypothetical. A previous alt-text pass on this site produced descriptions that were confidently wrong about how many people were in a photograph and what a room contained — errors that survived because the sentences were plausible and nobody re-opened the images. Reviewing against the source, not against the sentence, is what catches it.
Quality control that scales
Reviewing every generated value defeats the point; reviewing none guarantees drift. What works in between:
- Review at the point of creation. A suggestion the editor accepts or edits in one action costs seconds. The same correction six months later costs a project.
- Sample the backfill. When generating metadata across an existing library, review a random sample per batch rather than a fixed prefix — the first fifty items are rarely representative.
- Assert the mechanical rules in code. Length limits, no truncation mid-word, no trailing ellipsis, a complete sentence, no placeholder text. These are cheap to check automatically and catch a surprising share of defects.
- Spot-check the highest-visibility items by hand. Alt text on the homepage hero deserves a person's attention in a way that alt text on a 2019 PDF does not.
Sitting on a library with missing metadata? Schedule an AI CMS consultation — a backfill is usually one of the quickest wins available.
Human validation, and what it is actually for
The reviewer is not there to improve the prose. They are there to answer one question: does this value accurately describe the thing it is attached to? Framing review that way keeps it fast and keeps attention on the failure that matters.
It also means the reviewer needs the source in front of them. Reviewing alt text without seeing the image, or a summary without the article, is a checkbox exercise that will approve fabrications.
Governance considerations
Generated metadata is published content and inherits the same obligations. Three points worth settling:
- Voice. Descriptions are read by customers and appear in search results. They should follow the same style standard as the rest of your content, which means the standard has to exist.
- Accuracy is a claim. A meta description is a public statement about what a page offers. Getting it wrong is a small misrepresentation, not a technicality.
- Regeneration policy. Decide whether values are regenerated when content changes, and whether regeneration overwrites human edits. Silently overwriting a corrected value is a way to reintroduce the same error repeatedly.
These sit within the broader picture set out in AI content governance rather than replacing it.
How LABUSA approaches it
We usually start with an audit: what proportion of content has a usable description, what proportion of images has meaningful alt text, and where the gaps concentrate. That produces a backfill plan with a visible finish line, and it frequently improves search noticeably before any AI feature is added to the editing experience.
In building an intelligent content platform we put generation at the point of authoring rather than as a separate batch job, because metadata created alongside the content is reviewed while the author still has the context to judge it.
Frequently asked questions
Will generated metadata hurt our SEO?
Not inherently — a good generated description outperforms a missing one substantially. What does hurt is generated text that misrepresents the page, or identical descriptions across many pages, which is a sign the generation was not grounded in each page's actual content.
Is AI alt text good enough for accessibility?
It is a large improvement on nothing and a poor substitute for a person who knows what the image shows and why it is on the page. Use it to eliminate empty alt attributes, then review the images that carry meaning.
Should we backfill our whole library?
Prioritise by traffic and importance. A neglected 2018 PDF and your top landing page do not warrant the same care, and treating them identically wastes the review capacity you have.
What if content changes after metadata is generated?
Flag the metadata as potentially stale and re-suggest. Decide in advance whether that overwrites human-edited values — the safe default is that it does not.
How is this different from classification?
Generation writes new text; classification chooses from an existing list. Different failure modes and different controls — see AI content classification.
Related reading
- AI Content Classification — assigning content to a controlled vocabulary.
- AI Content Automation — where metadata generation sits among automatable tasks.
- AI-Assisted Editorial Workflows — generating metadata during authoring rather than after.
- What Is an AI-Powered CMS? — why structure and metadata underpin everything else.