--- description: AEO content planning starts with a prompt set: the questions buyers actually ask AI assistants across awareness, consideration, evaluation, and decision.… image: https://www.leanlabs.com/hubfs/LL%20V4.1/LL%20Featured%20Image.png title: How do AEO teams decide what content to create? --- Answer Engine Optimization How do AEO teams decide what content to create? # How do AEO teams decide what content to create? AEO content planning starts with a prompt set: the questions buyers actually ask AI assistants across awareness, consideration, evaluation, and decision. Those prompts get run against the engines to see who is cited today, which produces a gap list. Gaps are then ranked by commercial value, so comparison, alternatives, and pricing questions usually outrank generic definitions, with priority going to questions where the company can contribute original proof. ## Start with prompts, then find the gaps The prompt set replaces the editorial brainstorm. HubSpot built its program by mapping buyer questions across awareness, consideration, evaluation, and decision, then measuring visibility, share of voice, citations, and citation share against each one ([HubSpot AEO case study](https://blog.hubspot.com/marketing/hubspot-aeo-case-study)). That map shows you which prompts already name you and which ones name a competitor in the slot you wanted. The most useful category is the third one: prompts the engine currently answers without any good source behind it, where the first credible answer published tends to get adopted and then repeated. We run the same exercise before writing anything: 50 to 100 real prompts, scored across ChatGPT, Claude, Gemini, and Perplexity, with the current answer captured verbatim. The verbatim capture is what makes it worth doing, because watching an engine confidently describe your product in a competitor's framing produces a sharper content brief than any keyword tool is going to hand you. ## How we rank the gaps Not every gap is worth closing. We weight them roughly like this: | Gap type | Typical priority | Why | | ---------------------------------------- | ---------------- | ----------------------------------------------------------------- | | Comparison, alternatives, "best X for Y" | Highest | Buyer is shortlisting; the answer names vendors | | Pricing, cost, contract structure | High | High intent, and most competitors hide the answer | | Integration, fit, "does it work with…" | High | Disqualifying question; a wrong AI answer kills the deal silently | | Process, methodology, "how do you…" | Medium | Demonstrates expertise, feeds consideration prompts | | Category definitions | Foundational | Cheap to win, establishes entity, low direct intent | Definitions still get written early, because they're how an engine learns what you are and they're inexpensive to produce, though they rarely deserve more than the opening weeks of a plan. In our 90-day plans we typically front-load definitions and comparisons in the first three weeks, then spend the rest of the runway on the commercial prompts where a citation actually changes a deal. ## The proof test The last filter is the one that kills the most ideas. For each candidate question we ask what we can say that nobody else can, whether that's a number out of our own client data, a process we actually run on Monday mornings, or a case study with a client's name attached to it. When the honest answer is that we'd be paraphrasing what's already published, we don't write the page. This is also where the format changes. Kevin Barber, our Head of AI Growth, has made the same argument about format: "AEO is substantially different than traditional SEO because instead of long-form pages, you're focusing on semantic chunks that answer questions in bite-sized components." A 4,000-word guide that buries its answer in section six is difficult for a model to extract from, so we take the same expertise and organize it as discrete question-and-answer units in an [answer hub](https://www.leanlabs.com/faq/loop-marketing), which gives the engine clean passages it can lift without needing to reconstruct the argument. ## Where search demand still fits Prompts and keywords work as complementary inputs, and we run three streams together: the language sales hears on calls and in the CRM, the citation gaps from the baseline, and search demand data from Semrush to sequence what gets written first. Search volume is a decent proxy for how many people are asking a question in any interface, and it's the only one of the three with a long history behind it. The blend is what makes the calendar defensible. CRM language keeps the content in the buyer's actual words instead of internal product language, while the citation gaps aim it at the questions where we're currently absent. Demand data is the sanity check that stops us from writing a genuinely great answer to a question three people a year ask. It's the same sequencing logic behind how we build [content programs](https://www.leanlabs.com/solutions/loop-marketing) generally, applied to a set of prompts instead of a set of keywords. ### Ask AI about Lean Labs [ ![](https://www.leanlabs.com/hubfs/LL%20v5/Images/Logos/chatgpt.svg) ChatGPT ](https://chat.openai.com/) [ ![](https://www.leanlabs.com/hubfs/LL%20v5/Images/Logos/googlegemini%201.svg) Gemini ](https://www.google.com/search?udm=50) [ ![](https://www.leanlabs.com/hubfs/LL%20v5/Images/Logos/perplexity%201.svg) Perplexity ](https://www.perplexity.ai/search/new) [ ![](https://www.leanlabs.com/hubfs/LL%20v5/Images/Logos/claude%201.svg) Claude ](https://claude.ai/new) [ ![](https://www.leanlabs.com/hubfs/LL%20v5/Images/Logos/copilot.svg) Copilot ](https://copilot.microsoft.com/) Ask AI ```json { "@graph": [ { "@id": "https://www.leanlabs.com/#organization", "url": "https://www.leanlabs.com/", "logo": { "url": "https://www.leanlabs.com/favicon.ico", "@type": "ImageObject", 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