Step 1 — Baseline: measure before optimising
Before any action, take an honest snapshot of your site's current visibility across AI surfaces. Without a baseline, no iteration is credible.
- Pick 10 to 20 strategic queries covering top, middle and bottom of funnel.
- Run each on ChatGPT, Perplexity, Gemini, Claude and Google (AI Overviews).
- Note for each query: sources cited, presence or absence of your brand, quality of the answer (accurate, biased, outdated).
- Log results in a dated table.
Complement with Google Search Console (AI Overviews report when available), and, if possible, a monitoring tool (Profound, Peec AI, Otterly, Scrunch, AthenaHQ).
Step 2 — Structure content for retrieval
AI engines split documents into chunks and index passages. Poorly structured content becomes invisible, even if good. Operational rules:
Minimal structure of a GEO-optimised page
- A unique H1 carrying the primary query, clear and jargon-free.
- H2s segmenting distinct intents, not verbose chapters.
- Paragraphs of 3 to 6 lines, one idea per paragraph.
- Standalone sentences: readable when extracted from context.
- Numbered lists for steps, bulleted lists for enumerations.
- Tables for comparisons — AI engines read clean HTML tables well.
- A definition at the first occurrence of every technical term.
- A summary up top, an actionable recap at the end.
See the dedicated page on content structure for LLMs.
Step 3 — Consolidate semantic authority
LLMs reason in entities. To be cited on a topic, your site must be recognised as a legitimate source on that topic. Levers:
- Topic clusters: one pillar, 5 to 10 satellites, strong internal linking.
- Entity co-occurrence: pages cite domain markers (competitors, tools, methods) with nuance.
- Disambiguation: your brand is tied to its expertise consistently on- and off-site (Wikipedia, social profiles, directories).
- Inbound links from topically aligned sites. One relevant link beats ten generic ones.
- schema.org:
Organization,Article,FAQPage,BreadcrumbListproperly formed.
Step 4 — Open the site to AI bots (or arbitrate) and reinforce the technical layer
Crawl is a necessary condition. Check:
- Explicit robots.txt: GPTBot, PerplexityBot, ClaudeBot, Google-Extended, OAI-SearchBot, Applebot-Extended allowed (or not, by deliberate decision).
- CDN / WAF / Cloudflare: verify AI bots aren’t blocked by default (the 2024 "block AI scrapers" rules are often on by mistake).
- Rendering: prefer SSR / SSG; not all LLMs execute JS.
- Performance: LCP < 2.5 s, CLS < 0.1, INP < 200 ms. AI bots cap wait times.
- Clean XML sitemap, absolute canonical, validated schema.org.
- HTTPS, HTTP/2 or HTTP/3, compression.
- llms.txt: useful to surface key content, not a prerequisite.
Step 5 — Make content citable
A page can be well-indexed and never cited if it offers no extractable claim. LLMs prefer passages that stand alone and carry clear informational value.
- Sharp statements. Avoid "it may appear that", "some think that".
- Dated and sourced figures. A figure without a source is suspicious; a figure without a date ages badly.
- Primary-source citations. LLMs reward pages that cite other credible pages.
- Repeatable formats. Answer a question with a firm paragraph, followed by an example and a nuance.
- Identified author. A page with well-filled
Organizationand/orPersonschema.org reinforces trust.
Step 6 — Measure, iterate, maintain
AI optimisation is a long-run discipline, not a sprint. Recommended cadence:
| Frequency | Action |
|---|---|
| Weekly | Log review for AI User-Agents, fix technical errors. |
| Bi-monthly | Re-run the 20 strategic queries on main AI engines. |
| Monthly | Review tracked citations (third-party tool), prioritise pages to enrich. |
| Quarterly | Dated refresh of each pillar page, full audit via the LOOP checklist. |
| Biannual | Content strategy review, cluster adjustment. |
Where to start, concretely
If you can only do one thing: audit a pillar page (your strongest page on a strategic topic) with the LOOP checklist, fix it in half a day, and re-test on AI engines 3 weeks later. That's the test with the highest learning per hour invested.