Guides · 11
Every Citable guide on Answer Engine Optimization, Generative Engine Optimization and LLM-readable content, grouped by topic. Each opens with a direct answer and lists its sources.
What answer engines are and how they choose what to cite.
Search Engine Optimization (SEO), Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) describe three stages of the same job. SEO optimises a page to rank in a list. AEO optimises a passage to be extracted as a direct answer. GEO, from a 2023 paper, optimises a source to be cited inside an answer written by a language model. Crawlability and quality carry over; the unit, reward and pipeline change.
8 min read
Answer Engine Optimization (AEO) is the practice of structuring content so that AI-powered answer engines — ChatGPT, Perplexity, Claude, Copilot and Google's AI Overviews — can retrieve it, understand it and cite it inside a generated answer. Where classic SEO competes for a ranking position, AEO competes for extraction: a self-contained passage that resolves one question, from a page with a clear author, date and sources.
6 min read
Writing answer-first pages that models can quote verbatim.
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's description of a reliable source, and it is not a ranking factor. For answer engines it translates into signals a machine can verify: a named author with a profile page and Person markup, visible published and updated dates that match the structured data, outbound links to primary sources with a citation list, first-hand evidence, and an About page that says who is responsible.
8 min read
Answer-first content opens every page and every section with a self-contained answer of roughly 40–70 words, then explains, qualifies and expands. The first sentence names the main entity, each heading carries exactly one question, and no passage depends on a pronoun or a paragraph elsewhere. Written this way, a passage still makes sense after an answer engine splits the page into chunks and retrieves one of them in isolation.
8 min read
Schema.org, JSON-LD and the entity signals machines read.
FAQ content works for answer engines when each item pairs a question people ask with a self-contained answer of 40–80 words, rendered in the HTML rather than loaded on click, and mirrored in FAQPage JSON-LD from the same fields. Google restricted FAQ rich results in 2023 and withdrew them in May 2026, but FAQPage remains valid schema.org, and question-and-answer pairs are the passages retrieval systems extract.
8 min read
Structured data for AI answers is JSON-LD that labels what a page contains: Article or TechArticle for provenance (author, datePublished, dateModified, citation), FAQPage for question–answer pairs, DefinedTerm for definitions, Person with sameAs for authors, BreadcrumbList and WebSite for context, and SpeakableSpecification for the direct answer. Google requires no markup for AI features; its value is removing ambiguity, so generate it from the CMS fields that render the visible text.
9 min read
Crawler access, llms.txt, Markdown delivery and rendering.
A headless CMS content model for answer engines makes every Answer Engine Optimization signal a constrained field, not an editorial habit: a required short answer with a maximum length, key takeaways, FAQ items, sources with URL validation, the author as a relation, and published and updated dates. JSON-LD, Markdown and llms.txt are then generated from those fields, so no representation can disagree with the page.
9 min read
Serving Markdown to LLMs means publishing a plain-text representation of each page next to the HTML one, so that AI crawlers and agents can read it without rendering JavaScript or spending tokens on markup. There are two delivery patterns: a .md suffix on the canonical URL, and content negotiation on the Accept: text/markdown request header, with Vary: Accept and a Link rel=canonical header pointing back to the HTML page.
10 min read
AI crawlers are controlled through robots.txt user-agent groups, and every major vendor runs several agents. OpenAI and Anthropic separate a training crawler (GPTBot, ClaudeBot), a search indexer (OAI-SearchBot, Claude-SearchBot) and a user-triggered fetcher; Perplexity runs PerplexityBot and Perplexity-User. Google's AI Overviews use ordinary Googlebot; Google-Extended only opts out of Gemini training and grounding. Allow the search agents if you want citations, and treat user-triggered fetchers as outside robots.txt.
9 min read
llms.txt is a proposed convention, published by Jeremy Howard of Answer.AI in September 2024, for a Markdown file at /llms.txt that gives language models a curated index of a site: an H1 title, a blockquote summary and H2 sections of links with one-line descriptions. No major AI provider has confirmed reading it, but it costs almost nothing to generate from a CMS and is widely published by documentation sites.
8 min read
Tracking AI citations, referrals and share of answer.
AI visibility is measured from four partial signals, because no answer engine reports citations directly. Referral traffic identifies clicks from chatgpt.com, perplexity.ai, copilot.microsoft.com, claude.ai and gemini.google.com; server logs show which AI crawlers fetch which pages; a fixed panel of prompts run on a schedule gives citation rate and share of answer; and Google Search Console reports AI Overview clicks only blended into Web search totals.
8 min read