AI visual explainers need respect for the reader

A generated diagram can make an article look explained before it has explained anything.

That is the editorial problem with cheap visuals. They add rhythm to a page, but they can also hide invented labels, meaningless arrows, fake UI states, and architecture that only works because the reader is being polite.

Before adding one, I ask a blunt question: what gets harder to understand if the visual disappears?

If the answer is “nothing,” the image is decoration. Decoration is allowed. It just should not borrow the authority of an explainer.

A technical visual should answer a specific question:

• what order do these operations happen in?
• where does data cross a boundary?
• how does this state change?
• what does this comparison look like?
• which value changes the result?

For a chunking example, a table may teach more than an illustration:

chunk size: 200 tokens
overlap: 40 tokens
result: 18 chunks
risk: section boundary split across chunks 7 and 8

That is less beautiful and more useful.

Screenshots need the same discipline. Show the empty result if the post is about empty states. Show the approval prompt with the actual resource if the post is about permissions. Show the failed trace, the long label, the diff, or the dead-letter queue. A screenshot that contains only the happy path is often a product advertisement, not evidence.

When I review a generated diagram, I check whether the labels are real terms from the article, whether every arrow has a meaning, whether the visual invents components the text never explains, whether it works at small size, and whether its meaning depends on color. I would rather publish a plain table than a beautiful diagram that lies.

Interactive visuals can be better when the topic involves state or parameters. Let the reader change a weight, invalidate a cache key, move a ranking score, or fail a worker. Keep the controls few and the relationship visible. Five knobs with no obvious consequence is not an explainer.

The reader is already doing work: holding the argument, mapping terms, and deciding whether to trust it. A visual earns its space when it reduces that work. AI made images cheaper. It did not make the reader’s attention cheaper.