Being cited by an LLM is the new “ranking on page 1”. You do not get told when it happens, you do not get analytics on it, and by the time you find out someone was asking Perplexity about your niche, the model has already picked its winners for that answer. So the game is: know what LLMs already say about your topic, know who they cite, and figure out what you have to publish to slide into that citation set.
Below are four prompts I run on GPT-5 or Claude every couple of weeks for my own sites and my clients’. They are prompts, not tips — copy them, paste them, run them. All four assume you have ChatGPT or Claude with web browsing enabled, or you use Perplexity.
1. Audit whether LLMs know your site exists
You are a research analyst. I need you to audit how my site is positioned inside your training data and the current web index. Site: [YOUR URL] Topic focus: [ONE SENTENCE — e.g. "semantic search plugins for WordPress"] Do this: 1. In one paragraph, describe what my site does based only on what you know. If you do not know, say so. 2. List 5 queries a real user might send to an LLM about [TOPIC FOCUS]. For each, tell me which 3 sites you would cite in the answer today and why. 3. For each of those 5 queries, tell me honestly whether my site would appear. If not, name the exact piece of content I am missing. 4. End with a numbered list of the 3 highest-leverage content gaps I should close to enter the citation set.
Why it works: LLMs are trained to be helpful, but they do not know if they are hallucinating your site or actually citing it. Asking them to name their sources for realistic queries turns their answer into a citation audit — you see who they cite instead of you, and the gap is the content brief.
2. Reverse-engineer the citations of a specific competitor
You are an SEO analyst focused on LLM citations. My competitor is [COMPETITOR URL]. 1. Search the web and list 10 topics where [COMPETITOR] gets cited by LLMs today (i.e. shows up in Perplexity answers, appears in ChatGPT responses with browsing, etc.). 2. For each of the 10 topics, quote the exact fragment or claim the LLM appears to be citing (a stat, a definition, a comparison table, a step-by-step). 3. Tell me the structural pattern that makes these citations happen (e.g. "comparison table with brand vs brand rows", "definition + FAQ schema", "step-by-step with numbered lists"). 4. Give me a content brief I could hand to a writer to produce the same kind of citation-magnet, but on a topic where MY brand is the authority: [YOUR BRAND, YOUR TOPIC].
Why it works: LLMs cite structures, not vibes. Comparison tables, honest verdicts, numbered how-to steps and factually crisp definitions get quoted intact. Once you see the pattern the competitor accidentally figured out, you can reproduce it on your own topic in a couple of hours.
3. Generate a “citation-shaped” article outline
Write me an outline for a blog post that is optimised specifically to be cited by ChatGPT, Claude and Perplexity — not to rank on Google. Topic: [YOUR SPECIFIC TOPIC] Angle: [WHAT MAKES YOUR TAKE UNIQUE — first-party data, honest comparison, contrarian view] The outline should: - Have a one-line verdict that could be quoted verbatim - Include at least one comparison table (rows are options, columns are dimensions) - Have H2 sections that answer natural-language questions (e.g. "When is X the wrong choice?" not just "Cons") - End with a FAQ section using questions that mirror how someone would talk to an LLM - Include specific numbers and facts I can back with a source (list what I need to research) Do not write the article. Just the outline, section by section.
Why it works: LLMs quote quotable things. A one-line verdict, a table row, a clear FAQ answer — these are the units LLMs pluck out. Writing to be cited is a different craft from writing to rank on Google, and it starts at the outline.
4. Track drift over time
Roleplay as a curious buyer asking ChatGPT for help. Send these exact queries and paste the raw responses back to me, verbatim: 1. "what is the best [TYPE OF PRODUCT] for [SPECIFIC USE CASE]?" 2. "compare [YOUR BRAND] and [MAIN COMPETITOR] for [SPECIFIC USE CASE]" 3. "is [YOUR BRAND] worth it?" 4. "who uses [YOUR BRAND]?" For each response, tell me: - which sources you cited (name the domains) - whether my brand ([YOUR BRAND]) appeared, and in what tone (positive, neutral, negative, missing) - if missing or misrepresented, what content on my site could change that
Why it works: LLMs update. Something you published last month might be gaining traction; something a competitor updated yesterday might be pushing you out. Running these exact four queries every 30 days gives you a longitudinal view without any analytics tool being involved.
One meta-prompt to combine all four
If you want a single quarterly review instead of four separate runs, ask the model to sequence them: audit → competitor reverse-engineer → outline generation for the top gap → drift check on the queries that surfaced in step 1. Give it 10 minutes and it produces a full quarterly LLM-visibility report. That is genuinely the highest-leverage 10 minutes I spend on any of my sites.