Generative Engine Optimization: What the Data Actually Supports in 2026

Описание и применение

<blockquote> <p>TL;DR: The order that matters is access, entity, shelves, placements. Placements are the main lever and the slowest. llms.txt is placebo. General schema markup does not move citations. Nobody can guarantee you a hit rate, and the lag from publication to appearing in answers runs four to eight weeks. Everything below is sourced.</p> </blockquote> <p>Generative Engine Optimization is eighteen months old as a category and almost entirely unpoliced. That combination produces a lot of confident decks. This post is the version of the playbook we actually run, with the evidence attach

Задача

<blockquote> <p>TL;DR: The order that matters is access, entity, shelves, placements. Placements are the main lever and the slowest. llms.txt is placebo. General schema markup does not move citations. Nobody can guarantee you a hit rate, and the lag from publication to appearing in answers runs four to eight weeks. Everything below is sourced.</p> </blockquote> <p>Generative Engine Optimization is eighteen months old as a category and almost entirely unpoliced. That combination produces a lot of confident decks. This post is the version of the playbook we actually run, with the evidence attach

Результат

<blockquote> <p>TL;DR: The order that matters is access, entity, shelves, placements. Placements are the main lever and the slowest. llms.txt is placebo. General schema markup does not move citations. Nobody can guarantee you a hit rate, and the lag from publication to appearing in answers runs four to eight weeks. Everything below is sourced.</p> </blockquote> <p>Generative Engine Optimization is eighteen months old as a category and almost entirely unpoliced. That combination produces a lot of confident decks. This post is the version of the playbook we actually run, with the evidence attach

Доказательства

Generative Engine Optimization: What the Data Actually Supports in 2026

Источник