Generative Engine Optimisation: Getting Cited by ChatGPT and Perplexity

This post is Beacon's own generative engine optimisation approach made public — the same structural work we do for GEO & AEO client engagements, written out in full rather than kept as an internal playbook. If you have read general advice about "optimising for AI search" that stayed vague on specifics, this is meant to be the more concrete version.

How do AI answer engines actually decide what to cite?

Most AI answer engines — ChatGPT with browsing, Perplexity, Google AI Overviews — combine two steps: a retrieval step that pulls candidate content from a search index or live crawl, and a generation step where the model synthesises an answer and decides which sources to cite. The generation step favours content that is unambiguous and directly answers the implied question, because that is easier for the model to extract and attribute confidently. A page that states "X costs Y" in a single clear sentence gets quoted; a page that builds toward the same fact across three paragraphs of context-setting often does not, even though a human reader would find both equally informative.

Why do third-party listicles matter more than your own content?

Because AI engines appear to weigh corroboration — how consistently a fact appears across multiple independent sources — more heavily than traditional search weighs backlinks from a ranking standpoint. If your own site is the only place a claim about your business appears, an AI engine has less basis to trust and repeat it than if five independent, credible sources state the same thing. This means active work on getting mentioned accurately in industry roundups, comparison articles, and review sites is not a nice-to-have for AI visibility — it is closer to the centre of the strategy than your own blog content is.

This is a genuine mindset shift from traditional SEO, where the instinct is to build authority on your own domain. GEO asks you to build a corroborated presence across the web, with your own site as one node in that network rather than the only one that matters.

How should content actually be structured for AI extraction?

State the direct answer to a likely question in the first sentence or two of the relevant section, then elaborate afterward — the reverse of a narrative structure that builds toward a conclusion. FAQ sections with question-shaped headings and a direct opening answer are unusually well suited to this pattern, which is part of why we structure every service and location page on this site that way rather than as flowing prose alone.

Specificity also matters more here than for traditional SEO. A vague claim like "we deliver fast turnaround" is not something an AI engine can confidently cite as a fact; a specific claim like a named number, timeline, or comparison is. This is the same principle behind why this post opens each section with a direct claim rather than a lead-in paragraph.

What is llms.txt, and is it actually worth implementing?

llms.txt is an emerging convention — a plain-text file at a site's root, similar in spirit to a sitemap or robots.txt but written specifically to help AI crawlers understand what a site contains and where its most authoritative content lives. It is not yet universally adopted or confirmed to directly influence every major AI provider's retrieval behaviour, but the cost of implementing it is low, and the risk of being behind the standard once it is more widely used is a reasonable one to avoid cheaply now. We treat it as standard practice on every GEO engagement for this reason, not because its impact is fully proven yet.

Should we block AI crawlers to protect our content?

This is the question we see clients get backward most often. Blocking a crawler like GPTBot in robots.txt to prevent your content being used for model training also typically blocks that same crawler's live retrieval, which is what lets the AI engine cite your site when answering a user's question in real time. These are two separate decisions — whether to allow training-data collection, and whether to allow retrieval access for live answers — and conflating them into a single blanket block forfeits visibility in exactly the channel a GEO strategy is trying to win. If being cited by AI engines matters to your business, the retrieval-access question should usually stay open even if you have concerns about the training-data question.

Does GEO replace traditional SEO, or work alongside it?

Alongside it, and the overlap is meaningful — clear, well-structured, accurate content tends to help both traditional rankings and AI citation. But the specific techniques and what you measure differ: traditional SEO cares about keyword rankings and backlink profile, while GEO cares about extraction clarity and cross-source corroboration. Most businesses serious about search visibility now need both disciplines running together, not a choice between them — see SEO services for the traditional half of that pairing.

How do you actually measure whether GEO work is succeeding?

By testing real AI engine responses to a tracked set of buyer-relevant questions in your category, on a regular cadence, and recording whether and how your business is cited. This is the only reliable measurement method currently available, since none of the major AI providers currently offer citation analytics the way search engines offer rank tracking. It is more manual than traditional SEO measurement, but it is the honest way to know whether structural changes are actually moving the needle.