Three-part series on GEO published: optimizing for ChatGPT, Claude and other AI models

In July 2026, Habr published the first part of a trilogy on GEO (Generation Engine Optimization), the discipline of competing for content visibility in generative AI models such as ChatGPT and Claude. The author argues that the era of promotion in AI systems has already arrived: it is no longer theory or speculation.

How GEO differs from SEO

GEO is not simply an add-on to classic search optimization. It is a standalone field with its own ranking rules, its own success metrics and an entirely different logic. In search results (SEO), you compete for clicks: your link must be visible, your headline must catch the eye, and your snippet must persuade the user to click. In AI-generated answers (GEO), your content competes for something else entirely: getting into the training set or into the context window the model uses to generate a response.

Key differences:

• Target audience: in SEO, it is people searching for information; in GEO, it is the AI models themselves, which aggregate that information
• Metrics: SEO looks at clicks, conversions and time on site; GEO looks at mentions, citations and inclusion in the context window
• Ranking algorithms: SEO relies on domain authority and links; GEO relies on text parsing, chunk relevance and a source's authority in the eyes of the models
• Lifespan: SEO traffic can keep coming for years; GEO traffic is split among all the sources that make it into the generated answer

Why this is not a cosmetic add-on

In the original article, the author stresses that GEO is not SEO with a bonus. The fight for visibility in AI models follows entirely different rules, and ignoring it is risky. Content optimized for SEO can be invisible to ChatGPT or Claude. Conversely, material that is ideal for AI-generated answers can be invisible to classic search engines.

Structure of the series: from theory to practice

The trilogy is split so that each part can be read independently:

1. Part 1 (published): the conceptual foundation: what happens in AI-generated results, what is being fought over, and why SEO recipes do not work 2. Part 2: the content layer: grounding in models, fact-checking, how AI models choose sources, and why AI-driven manipulation is doomed 3. Part 3: applied technique: chunking, markup, KPIs, and attributing traffic from AI models

What this means

GEO is becoming a separate marketing discipline. It used to be enough to optimize for Google and Yandex; now a further branch is needed: optimization for AI systems. Materials that ignore GEO gradually lose visibility in new traffic channels. Those who start working with GEO now will gain an edge over competitors.