What does "information gain" look like in practice when ten pages already answer the same search query?

Information gain comes from adding evidence, decisions, examples, or structure that changes what the reader can understand or do.

Before writing, inventory what the existing results already cover. Separate common baseline information from unanswered questions, unsupported claims, missing examples, outdated guidance, and contradictory recommendations.

Then choose a source of additional value. Useful options include first-party data, a real implementation walkthrough, before/after measurements, a decision framework, original screenshots or code, expert experience with constraints, a dataset, a calculator, a template, or synthesis across primary sources.

Do not confuse novelty with contrarianism. A surprising claim without evidence is not information gain. The contribution should be defensible and useful.

Also improve structure where existing content is hard to use. A clear diagnostic sequence or comparison table can add value even when the underlying facts are known, because it reduces the reader's decision cost.

Document methodology for original findings. Explain sample size, dates, assumptions, and limitations so other people can evaluate the evidence.

For commercial pages, information gain can mean exposing details competitors hide: implementation requirements, compatibility, limitations, pricing logic, service boundaries, or real examples.

The editorial test is: if your page disappeared, would the web lose any unique evidence, workflow, example, or synthesis? If the answer is no, the page probably needs a stronger reason to exist.