SEO & answer-engine guides

Practical playbooks on AI SEO, generative engine optimization, and getting cited by ChatGPT and AI Overviews — written by the people who build the platform.

Written by the MIKQA research team · Updated

In shortThese guides cover the tactics that move rankings in both traditional Google search and AI-powered engines — generative engine optimization (GEO), answer engine optimization (AEO), automated rank tracking, and white-hat link building. Written by the team that builds the platform.
FAQ

GEO & AI search, in plain English

GEO is the practice of structuring content so generative AI engines — ChatGPT, Google AI Overviews, Perplexity, and Gemini — cite it in their answers. It builds on traditional SEO with extractable answer blocks, structured data, and the authority signals AI systems trust when choosing sources.

AEO is structuring a page so any answer engine — a featured snippet, a voice assistant, or an AI summary — can lift one clean, direct response. It overlaps almost completely with GEO: the same self-contained answer blocks and schema serve both, so one set of work covers many answer surfaces.

They share most of their foundation — crawlability, authority, and helpful content matter to both. SEO optimizes to rank a link in the results page; GEO optimizes to be quoted inside an AI-generated answer. Google's own guidance calls optimizing for AI search "still just SEO", so GEO is a layer on top, not a replacement.

Open key sections with a 40–55 word direct answer, use question-style headings, add FAQ and Article schema, and build topical authority with relevant white-hat backlinks. Keeping facts current and sourced helps too, since AI systems favor fresh, citable passages. Our GEO guide covers the full playbook.

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