Fred Leise developed eleven heuristics for qualitatively evaluating a content collection: collocation (are similar things grouped together where people would expect to find them), differentiation (can people tell dissimilar collections apart), completeness (are there gaps compared to what competitors offer, and does referenced content actually exist), information scent (do labels and headings use the audience's own language), bounded horizons (does the structure make the scope of the space clear), accessibility, multiple access paths, appropriate structure (does it match how the audience actually thinks and searches), consistency (do similar page types share a consistent structure), audience-relevance, and currency (is the content kept up to date).
Each heuristic can be rated on a five-point scale, from strongly deviating to strongly conforming, which turns a qualitative impression into something you can compare across a whole content collection and prioritize.
Eleven heuristics can feel like a lot to hold in your head at once, but in practice, most real content problems trace back to just two or three of them showing up together.
1: Differentiation. This heuristic asks whether people can tell dissimilar collections apart, duplicate articles under different titles do the opposite, making the same content look like two different things.
2: Currency. References to a retired dashboard are precisely what the currency heuristic flags: content that hasn't been kept up to date, actively misleading anyone still relying on it.
3: Consistency. Similar content, troubleshooting articles, failing to share a consistent structure is a direct consistency failure, and one the source material specifically calls out as damaging for page types people rely on repeatedly.
There's a reasonable case for prioritizing currency first: outdated references to a retired dashboard could actively mislead a customer trying to fix a real problem right now, which is a more urgent risk than confusion or inconsistent formatting. But there's also a fair case for differentiation, since duplicate articles under different titles waste both user time and the support team's content-maintenance effort, and the confusion compounds every time someone searches and finds two competing "answers."Either answer can be defended, what matters is reasoning from actual user and business impact, not just picking whichever heuristic sounds most severe in isolation.
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Casts the AI as: You are a content strategist helping define quality criteria for a content audit.
Names 5 specific outputs to produce, so the response comes back structured rather than a general summary.
My documentation covers, Primary audience and Key user goals tell the AI what your specific situation is, not a generic one.
For each criterion, provide: a name, a plain-language definition, how to measure it, and an example of a page that fails it.