How is an AI content gap different from a normal SEO keyword gap?

A keyword gap is a phrase you don't rank for. An AI content gap is a question an assistant asked on a user's behalf that your site had no clean answer for. The difference matters because the assistant writes its own search queries, and they're usually longer and more specific than anything in a keyword tool. You can rank well for the head term and still lose every one of the questions underneath it.

Where do the questions actually come from?

Three places, and you want all three. Grounding searches are the web searches assistants run before they answer, so they show you what the model thinks it needs to know. People Also Ask gives you the phrasings Google already associates with your topic. Reddit and Quora give you how buyers ask each other, which is blunter and often closer to the real objection.

Do I need to write a new page for every gap I find?

No, and that's the most common mistake. Most gaps close with a section or a short Q&A block added to a page you already have. New pages are for gaps where you have nothing on the topic at all. Splitting one topic across two thin pages usually makes both of them harder to cite.

How many FAQs should I add to a page?

A few, and only on pages that warrant them. Google polices scaled content abuse at the site level, so blanketing every page with generic Q&A can hurt rankings everywhere, not just on the page you padded. A short block of questions the page really answers is worth more than a long block of questions it doesn't.

How long before I know whether closing a gap worked?

Plan on two to six weeks. The assistant has to recrawl the page, the search engine behind it has to re-rank it, and then the query has to come up again in tracked prompts. Anything you measure in the first week is mostly crawl timing, not content performance.

How to Find Content Gaps From Real AI Questions

To find content gaps from real AI questions, start with the grounding searches that ChatGPT, Gemini, Claude, and Perplexity run before they answer questions in your category, add the Google People Also Ask, Reddit, and Quora phrasings around those topics, then check each question against what you've already published. The ones with no good answer anywhere on your site are your gaps. Sort them by buying intent and how close your existing pages already are, close the top ones by adding a direct answer to the best page you already have, and track the same questions afterward to see whether the page starts ranking and then starts getting cited.

A single tracked prompt usually produces three to eight grounding searches, so 25 prompts will hand you 60 or more real questions in a week. About half of those gaps close with an added section rather than a new page, and a closed gap takes two to six weeks to show up in AI answers. The whole loop takes a couple of hours to set up and about thirty minutes a week to keep running.

Most content calendars are built the other way around. Someone exports a keyword list, sorts by volume, picks the terms nobody on the team has written about yet, and calls those the gaps. That worked when the reader typed the query themselves. It works a lot less well now, because when someone asks ChatGPT or Gemini a question, the assistant doesn't search for what they typed. It writes its own searches first, and those searches are longer, more specific, and far more revealing than the phrase that started the conversation.

Those generated searches are the best content brief you'll get. They tell you what the model believes it needs to know before it's willing to recommend anyone. If your site can't answer one of them, you're not in that answer, no matter how well you rank for the topic overall.

This guide works with any AI visibility setup; the callouts show how each step runs in Spyglasses.

Step	What you're doing	What tells you it's working
1. Collect questions	Pulling grounding searches and community phrasings into one list	40 or more real questions, not keyword stems
2. Map coverage	Matching every question to a page, or to nothing	Every question is marked answered, weak, or missing
3. Sort the gaps	Scoring by intent, closeness, and difficulty	A ranked shortlist you can work top down
4. Answer it	Adding the answer to the right existing page	The answer is findable without scrolling or clicking
5. Measure	Re-checking the same questions after publishing	The page enters the results, then starts getting cited

Collect the questions AI is actually asking

Start with grounding searches, sometimes called fan-out queries. These are the web searches an assistant runs in the background while it's composing an answer. Ask ChatGPT to recommend project management software and it might search for six or seven things you'd never have written down, like "project management software for agencies under 20 people" or "asana vs monday pricing 2026."

You can see them a few ways. Some assistants show their sources and searches directly in the interface, so running your own category prompts and reading what the model looked for is a free start. A visibility platform records them automatically across every prompt it tracks, which is the version that scales past a dozen questions.

Then widen the net with two more sources. People Also Ask gives you the questions Google already associates with your topic, which matters because ChatGPT and Gemini both search Google underneath, while Claude searches Brave. Reddit and Quora give you something different and more valuable: the way buyers ask each other, before anyone has cleaned up the phrasing. That's where you find the objection nobody puts in a search box, like whether the tool is worth it for a team of three.

Write them down as full questions. Not "agency pricing" but "how much do agencies charge for AI visibility work." The full question is what you're going to answer, and the phrasing carries information the stem throws away.

Map the questions against what you've already published

Now check each question against your own site. The goal is one of three marks per question, and the middle one is where most of the value hides.

Answered well means a page states the question or something very close to it, and answers it in a passage you could lift out and quote on its own. Answered badly means the information is technically on your site, but it's a sentence buried in the middle of a page about something else, or split across three pages, or written in a way that only makes sense if you already read the two paragraphs above it. Not answered means exactly what it says.

Search your own site the way a machine would. Use a site search for the key nouns in the question, not for the question itself, and read what comes back as if you'd never seen the page. If you can't find a clean answer in about fifteen seconds, a retrieval system won't either.

The "answered badly" pile is usually the biggest, and it's the cheapest to fix. Those are pages that already have the authority, the links, and the crawl history. They just don't have a passage a model can extract. Fixing one of those is an afternoon; writing a new page that earns the same authority is a quarter.

Pull quote: A content gap usually isn't a topic you never covered. It's an answer you buried three scrolls down a page about something else. — “A content gap usually isn't a topic you never covered. It's an answer you buried three scrolls down a page about something else.” — Spyglasses

Sort the gaps by what's worth writing

You'll end up with more gaps than time. Score each one on three things before you decide what to work on.

Intent. How close is this question to someone choosing a vendor? "What is retrieval augmented generation" and "best AI visibility tools for small agencies" are both real questions, but only one of them has a buyer attached. Decision-stage questions win almost every tiebreak.

Closeness. Do you already have a page that half answers this? A page sitting at position 12 for the query needs a rewrite. A page that doesn't exist needs a brief, a writer, and three months of patience.

Difficulty. Look at what's ranking for the question now. If the top results are all major publications or aggregator sites, that's a long fight. If they're thin blog posts and a Reddit thread, that's an opening.

Gap type	What it looks like	What to do	Typical effort
Buried answer	You cover it, but it's mid-page and hedged	Rewrite the passage so it stands alone	1 to 2 hours
Split answer	Three pages each hold a third of it	Consolidate onto the strongest page	Half a day
Wrong framing	You answer the topic, not the question	Add a heading in the reader's words	1 hour
Real hole	Nothing on the site touches it	New page, if intent justifies it	1 to 3 weeks
Out of reach	High intent, but authority sites own it	Skip for now, revisit after other wins	Zero, deliberately

That last row matters more than it looks. Deciding not to chase a question is a real answer, and writing it down keeps someone from re-proposing it every planning cycle. If you want the deeper version of picking one query and going all in on it, winning a target AI grounding search walks through that single-query approach end to end.

Answer the question where people ask it

Write the answer as a passage that survives being pulled out of the page. That means it states the question or a close paraphrase in a heading, answers it in the first two sentences, and doesn't depend on anything above it to make sense. If your answer starts with "as we mentioned earlier," a retrieval system just lost it.

Keep the answer short and specific. Numbers, ranges, and plain nouns get quoted. Adjectives don't. "Most agencies charge between $2,000 and$ 8,000 a month for this work" is citable. "Pricing varies based on scope and objectives" is a sentence a model will skip past on its way to someone who answered.

Then decide whether the page warrants a Q&A block underneath. Sometimes it does, especially on product and pricing pages where buyers have a predictable set of follow-ups. Sourcing those questions from People Also Ask and community threads keeps them grounded in what people ask instead of what you wish they'd ask; that's what FAQ generation is for.

Be careful here. Adding Q&A to every page is one of the fastest ways to get flagged for Google's scaled content abuse policy, and that penalty lands at the site level, not just on the page you padded. Few, specific, and clearly connected to the page. Five tightly relevant questions on your best page beats a hundred broad ones spread across the site, and if you can't explain why a question belongs on a page, it doesn't.

For gaps in pages that already get traffic, the mechanics of the rewrite are covered in optimizing existing content for AI citation. For anything new, run it through a pre-publish draft check before it ships.

Measure whether the gap closed

Closing a gap takes longer to show up than most content work. Three things have to happen in order: the assistant's search engine has to recrawl and re-rank the page, the page has to enter the results the assistant reads, and then a tracked prompt has to generate that search again and cite you. Two to six weeks is normal. A week is not enough data to judge anything.

Track it in that same order, because each stage tells you something different. If the page never enters the ranked results, the problem is the page. If it ranks but never gets cited, the problem is usually the passage, meaning the content is there but not in a shape a model wants to lift. Those are different fixes, and knowing which one you have saves a lot of guessing.

Keep the original question list. The measurement is literally "did these specific questions get better," and that comparison only works if you didn't rewrite the list halfway through. Add new gaps to the bottom as you find them instead of replacing the set.

The questions were there the whole time

The best part of this workflow is that you're not guessing what to write. The assistants publish their homework. They tell you exactly which searches they ran before deciding who to recommend, and the answer to a question you can see is a lot easier to write than the answer to a keyword you inferred.

Start with one prompt in your category and read what the model searched for. You'll find two or three questions you thought you'd covered and hadn't. Fix those first, keep the list, and let it grow. The gap list stops being a quarterly research project and turns into something you check on a Monday morning in about ten minutes.