An LLM content strategy is how you get your brand named in ChatGPT, Gemini, Perplexity and Claude answers: track prompts, find gaps, publish, re-check.
Short answer: it isn't a replacement for SEO. Google says its AI Overviews and AI Mode need no special optimization beyond normal SEO best practice, and most assistants now pull from live web results as well as training data. So the job is practical: find the buyer questions where AI names a competitor instead of you, publish the page that answers that question better, and measure whether the answer changes. Below is the framework, the tools and what each costs, checked on vendor pages on 5 October 2026.
What is an LLM content strategy?
It's the part of your content plan aimed at large language models (LLMs) – the systems behind ChatGPT, Gemini, Claude, Perplexity and Google's AI features. Some people call it generative engine optimization (GEO). The goal is to be one of the few brands an assistant names when a buyer asks "what's the best tool for X?"
Two things decide what an assistant says. The first is its training data, which updates slowly. The second is live retrieval: ChatGPT, Perplexity, Gemini and Claude can search the web, and Google says AI Overviews and AI Mode run several related searches (it calls this "query fan-out") before writing an answer (Google Search Central). The second route is the one you can influence this quarter, and it rewards the same things good SEO does: indexable pages that answer the question clearly.
There's early research on what helps. The 2023 GEO paper by Aggarwal et al. (published at KDD 2024) found that adding citations, quotations and statistics raised a source's visibility in generative answers by 30–40% on its benchmark, while keyword stuffing did poorly (arXiv). Treat that as a direction, not a guarantee.
How do you build an LLM content strategy?
Three steps, repeated every month.
Step 1: Audit what AI says today
Write 15–25 prompts a buyer would actually type, such as "best [category] tool for small agencies" or "[competitor] alternatives". Run each one in ChatGPT, Gemini, Perplexity and Claude, and in Google AI Mode if Google traffic matters to you. Use a fresh chat with memory turned off so your own history doesn't skew the answer, and run important prompts twice, because answers vary from run to run.
Log the date, the engine, whether you're mentioned, which competitors are named and which sources are cited. Doing this by hand works for a first pass. Past a few dozen prompts, a tool that re-runs them on a schedule saves hours and gives you trend data. For a step-by-step version, see how to run a content gap analysis using AI recommendation data.
Step 2: Find the recommendation gaps
A recommendation gap is a buyer prompt where AI names a competitor and not you. For each one, ask:
Which sources does the answer cite, and do any of them mention you?
Is what AI says about you accurate and current, including prices and features?
Do you have a page that answers this exact question, or only a page that touches on it?
Which of your real differentiators is missing from the answer?
You'll find more gaps than you can fix. Rank them by commercial intent and how beatable the current answer is – the AI recommendation gap scorecard gives you a simple 1–5 scoring sheet.
Step 3: Publish the page that answers the prompt
Write for the prompt, not the keyword. If the prompt is "best invoicing tool for freelancers", the page should say who it's for, what it costs, how it compares and where it's weaker, with dates on anything that changes. Formats that tend to get cited are comparison pages, pricing pages, how-to guides with real steps, documentation and original data.
A general AI writer can speed up the draft, but it doesn't know your product, prices or customers. Whatever you use, a person who knows the subject should check every fact before it goes live.
Which tools do you need?
You need something to track prompts and something to write and publish. Here's how the main options compare, from the vendors' own pages (checked 5 Oct 2026):
Approach | Examples | What it covers | Starting price |
|---|---|---|---|
Manual checks | Spreadsheet plus free AI assistants | Snapshots only. Fine for 15–25 prompts once a month. | Free, plus your time |
SEO suite add-ons | AI visibility next to your rankings. Writing sits in separate tools. | Semrush toolkit $99/mo per domain (25 tracked prompts). Ahrefs custom prompts from $50/mo. | |
Dedicated trackers | Daily prompt tracking, sources and recommendations. Wellows also drafts content. | Otterly.AI from $29/mo (15 prompts). Wellows from $29/mo. Peec AI: see vendor page. | |
Track, write and publish | Checks ChatGPT, Gemini, Perplexity and Claude for your keywords, drafts from your brand knowledge base and publishes to WordPress or a webhook (Shopify on Standard). | $49/mo Starter, $79/mo Standard |
For a wider comparison, see AI SEO Tools: The Definitive Overview.
What builds authority beyond your own site?
Assistants don't only read your website. Answers to "best X" questions often cite review sites, listicles, forums and news coverage. Three things help:
Third-party mentions. Check which sites the answers cite for your category, then earn a place on them through reviews, guest articles or digital PR.
Consistent facts. Your name, category, prices and key features should read the same on your site, review profiles and directories. Conflicting facts give AI a reason to hedge or leave you out.
Clear structure. Headings that match real questions, tables for comparisons and Schema.org markup make pages easier to parse. They help; they don't guarantee anything.
Don't try to feed models claims you can't back up. Assistants compare sources, and a false claim on your own site is a credibility problem with people as well as machines.
Frequently Asked Questions
How is an LLM content strategy different from SEO?
It builds on SEO rather than replacing it. SEO aims for a ranking on a results page. An LLM content strategy aims for a mention or citation inside an AI answer. The groundwork is the same – crawlable, indexed, genuinely useful pages – but you measure prompts and mentions as well as rankings and clicks.
How long does it take to see results?
No AI company publishes a timeline. Answers that rely on live search can change soon after a new page is indexed. Answers that rely on training data change more slowly. Re-check your gap prompts two to four weeks after publishing, and keep checking, because answers move on their own as well.
What content formats work best?
Pages that answer one question completely: comparisons, pricing pages, step-by-step guides, documentation, FAQs and original research. Specific facts, dates and cited sources beat general marketing copy.
Can a small business do this?
Yes. Start with 10–15 high-intent prompts and fix the top three gaps before adding more. A spreadsheet and the free tiers of the main assistants are enough for the first audit.
If you want a quick baseline before you build the spreadsheet, run the free AI visibility audit. It takes no signup and about 30 seconds, and shows whether AI models know your brand, who they recommend instead and which content gaps to close first.