u/keyworddotcom

We’ve been reading about how ChatGPT, Gemini, AI Overviews, etc., pick sources, and one thing keeps coming up: they often don’t seem to use the whole page.

Supposedly, if the actual answer is buried halfway down the article, there’s a decent chance it just gets ignored. So the classic “long intro, build context, then get to the point” format might be hurting you for AI visibility.

The weird exception seems to be FAQs. Even if they’re at the bottom, they still get pulled because each question and answer can stand on its own.

So we’re wondering if anyone has gone back and rewritten older posts with this in mind?

Like:

• moving the main answer to the top
• cutting the intro
• adding a TL;DR
• making sections more self-contained
• adding FAQs

If you’ve done this, did you see any difference in AI citations, AI Overview mentions, or traffic?

We've been going down a rabbit hole on something that's been bugging us for months. Wanted to post here to see if SEOs are noticing this in the wild.

We've been running side-by-side tests, taking a keyword, pulling the top 10 results from multiple serp scraper providers, including other rank trackers at the same time, then manually searching the same keyword in a real browser with personalization off (pws=0), matching the same location.

For some keywords, the gap is pretty wild. Sites ranking at #2 in a live browser search are completely absent from the top 10 across every automated data source we tested. Not ranking lower, just not there at all.

What makes it stranger is that all the automated sources agree with each other perfectly. It's not like one tool is off. They all return the same top 10. It's just that the live result looks different.

We've been trying to understand why, and the most likely explanation we've landed on is that since early 2025, Google has gotten much better at distinguishing real human traffic from automated requests, and is serving meaningfully different results to each. NavBoost (Google's ranking layer that uses real user engagement signals) would also explain part of it, since scrapers don't generate those signals.

A few things we've ruled out:

• It's not personalization. We tested with pws=0 and from clean sessions
• It's not one provider's bug since multiple sources all agree with each other, just not with the live result
• It's not isolated to one keyword or niche because we're seeing this pattern across different industries and locations

It seems to affect a subset of keywords, not all of them. The affected sites tend to be smaller, niche domains that are likely being boosted by real user engagement signals that scrapers simply can't see.

Anyone here seeing discrepancies between what your rank tracker reports and what you actually see when you search manually? Particularly for smaller or niche sites that might be ranking on engagement signals rather than pure authority?

Would love to know if this is something others have noticed or if we're looking at this the wrong way.

We had a technical SEO on our podcast recently, and he mentioned seeing more and more sites built with AI coding tools (Lovable, Bolt, v0, that whole space), and while they look great, most of them are essentially invisible to search engines and LLMs.

The reason is that most of these tools generate single-page applications that rely on client-side rendering. When someone visits the site in a browser, JavaScript loads and builds the page content on the fly, so everything looks perfect. But when a search engine crawler or ChatGPT visits the same URL, they get a mostly empty HTML shell. The actual content isn't there.

The irony is that people are using AI to build their sites faster than ever, and those same sites end up invisible to AI search.

Something worth noting is that some of these tools have recently started addressing this. For example, Lovable added server-side rendering for new projects from mid-May 2026. But anything built before that, and most sites from other tools, probably still have the problem.

Many founders, freelancers, and small teams have shipped sites using these tools over the past year. And some of them are probably tweaking content, rewriting copy, or trying AI optimization tactics when the real issue is that nothing they publish is being seen in the first place. It's a build problem. 

Has anyone here dealt with this? If yes, did you notice a difference in visibility after going from a vibe-coded site to something server-rendered?

One recommendation is to avoid channel-by-channel planning.

Instead, start with a handful of high-intent keywords that actually matter to the business. Pick 20–25 money keywords that actually drive the pipeline and for each keyword, map where that search already shows up:

• What pages rank in Google
• What Reddit threads rank
• What YouTube videos appear
• What sources, AI Overviews/ChatGPT/Perplexity, mention
• What review sites show up

Then build around that same keyword across every channel.

Let's say one of those keywords is the ‘best keyword research tool’

• Let your SEO team build the landing page, comparison content, or blog post, then
• Your Reddit team focuses on finding the threads ranking for that query and joins the relevant conversations when it’s helpful.
• Your YouTube team creates comparison videos and tutorials around the same topic, and
• Your review-site team works on listings, reviews, and category positioning.

When you do this and treat SEO, Reddit, YouTube, review sites, and AI search as an integrated strategy, everybody has the same common north star. They’ll fuel the same category term rather than creating a bunch of disconnected content.

It also makes AI search tracking easier because you're focused on a small set of important queries rather than hundreds of random prompts.

It's a simple framework for approaching Search Everywhere Optimization, with the goal of being everywhere that matters for search.

Wanted to share something that feels lowkey overlooked in AI search: JavaScript.

TL;DR: A lot of AI crawlers don’t reliably retrieve content from JavaScript-heavy websites. So even if your page has the perfect answer, the crawler might not actually be able to pull it.

So, before obsessing over prompts, citations, and brand mentions, check this first: Right-click your page → View Page Source → search for your actual content. If your copy isn’t there, that’s a problem.

What we’d recommend:

• Use server-side rendering if possible
• Use static HTML for blog posts, docs, and landing pages
• Use pre-rendering if your site is JS-heavy
• Make sure the main answer/content is visible in the raw HTML

Feels basic, but it could be the reason good content isn’t getting picked up by AI tools.

We came across an interesting approach from an SEO agency founder we interviewed recently, and are curious if anyone here has tried something similar.

The basic problem was: how do you get your first few SEO clients when you don’t have case studies yet?

Instead of trying to convince businesses with theory, he built his own lead-generation websites first. One example was a local cosmetics site targeting searches around things like Botox and lip fillers. He ranked the site locally, got it generating actual inbound leads, and then used that as the pitch.

The pitch to local clinics was basically: “I already have a site generating leads for these services. If we work together, I can pass those leads to you as part of the package, then replicate the same strategy for your own site.”

That feels like a much easier sell than “trust me, I know SEO.” The business gets value up front, and the SEO has proof that they can rank something and drive leads before asking for money.

Once he got clients from that, he turned the projects into public case studies on LinkedIn, which helped attract more clients. He also added a referral offer where existing clients got 15% for any ongoing business they referred.

 It felt like a smart way to get around the “no case studies yet” problem, because the lead-gen site becomes the proof.

Has anyone here tried this or something similar?

We see a lot of them acting like they’re all-in on AI, but the real picture seems much messier.

AI tools aren’t cheap. They’re not always easy to set up properly. And if you’re trying to introduce them across a team, there’s also the whole upskilling/training side of it.

We’ve also heard from agencies that built or vibe-coded internal tools, used them for a bit, and then… never really went back to them. Curious: if you work at or run an SEO agency, what does AI/automation actually look like in your day-to-day?

Are you using it for reporting, content, keyword research, audits, QA, internal ops, client communication, or something else? And, more importantly, what’s actually working vs. what sounded like a good idea at first but didn’t work out? 

We’ve been noticing something interesting while tracking AI referral traffic.

ChatGPT (and similar LLMs) are sending users to URLs that don’t actually exist on the site, but are close enough to real pages that they look valid.

When users click those, they land on a 404 and drop off, and we end up losing high-intent traffic without even realizing it.

What’s happening here is that LLMs don’t always link to exact URLs. They sometimes generate paths that approximate the right page, but miss the exact structure.

If AI is sending you traffic, there’s a good chance some links are breaking like this

Quick way to check:

• Pull landing pages in GA4 filtered by ChatGPT / AI sources
• Export the URLs
• Run a crawl using the site audit tool 
• Check status codes
• Filter for 404s

Those are essentially hallucinated URLs.

Fix:

• Map those URLs to the closest real pages
• Implement 301 redirects

Feels like one of those early AI-search quirks that’s easy to miss if you’re not looking for it

The first place we’d start is Google Search Console. Filter for longer queries, around 30–35+ characters. What you’ll usually find is a bunch of messy, natural-language searches, which is useful because that’s pretty close to how people ask questions in ChatGPT, Perplexity, Gemini, etc.

For example, queries like “how to check my website ranking on Google,” “how to find what keywords competitors are using,” or “best way to track local rankings for clients” are basically raw prompts. You can clean them up slightly so they sound more like actual AI search prompts. So something messy like “best ai rank tracker agency team 5 people” becomes “What’s the best AI rank tracker for a 5-person SEO agency?”

From there, you can expand each prompt with ChatGPT or Perplexity. Take one seed question like “What’s the best AI rank tracker?” and look at the follow-up questions the tools suggest. You’ll usually find ideas around comparisons, features, pricing, and specific use cases. One prompt can quickly turn into questions like “Which AI rank tracker is most accurate?”, “What tools track AI Overviews?”, “What’s the best AI rank tracker for agencies?”, or “How do I track brand mentions in AI search?”

The last step is to pull language from real communities. Search your topic in Reddit, Slack groups, forums, LinkedIn comments, or wherever your audience talks. Look for question-style posts, tool comparisons, complaints, feature requests, and “is there a tool that…” comments. The comments are often more useful than the original post because that’s where people describe the actual problem in their own words.

For example, if someone says, “We need something that tracks both AI Overviews and ChatGPT citations,” that can become “What tools track both AI Overviews and ChatGPT citations?” That’s a much better prompt than something generic like “AI SEO tool.”

By the end, you’ll have a prompt cluster based on your own GSC data, LLM follow-up questions, real community language, and actual pain points. You can easily get 100+ solid prompts this way in under an hour without using a dedicated AI SEO tool.

Obviously, tools help once you want to scale tracking and reporting. But for the research phase, this manual method is a pretty good starting point.