Glossary

What is Information Gain?

The new, non-redundant information a page adds versus what's already published — increasingly the deciding factor in AI citation selection.

Definition

Information gain is the measure of what a page adds beyond existing coverage: new data, new analysis, new examples, a first-hand test no one else ran. Google holds a patent on scoring it, and the concept has become central to AI-era content strategy.

The opposite is redundancy — the 41st article saying what 40 others said, which a retrieval system has no reason to select.

Why it matters

An AI answer is assembled from a handful of sources. Every source must earn its slot by contributing something the others don't. Original statistics are the clearest gain — the Princeton GEO study found adding statistics increased source visibility in generated answers by double digits. One real benchmark, survey, or dataset routinely out-earns dozens of me-too posts in citations.

Frequently asked

How do I create information gain without a research team?

Publish what only you can: your product's aggregate usage patterns (anonymized), a hands-on comparison you actually ran, customer problem taxonomies from support tickets, teardown notes. Small-N original beats large-N derivative.

How do I audit content for information gain?

For each page ask: if this disappeared, would any AI answer lose a fact? If the answer is no, the page is redundant to machines — consolidate or add primary material.

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