Just as we were wrapping our heads around AI Overviews, Google unveiled another new search experience: Web Guide.
It’s a big change in how Google interprets intent and presents information. Think of it as a dynamically-generated, “magazine” SERP, that curates AI summaries and organic results.
What’s different about Web Guide is that—unlike AI Overviews or AI Mode—it actually encourages users to click, which makes it the most website-friendly AI search feature Google has shipped so far.
Is this the click comeback we’ve all been waiting for?!
Here’s what Web Guide is, how it works under the hood, and what you can do to optimize for it.
But first, here’s a quick peek of what it looks like O.O
Google Web Guide is a Search Labs experiment that uses a custom version of Gemini to organize search results into themed groups, rather than displaying the traditional 10 blue links.
Google launched Web Guide on July 24, 2025 as an opt-in experiment. It originally appeared only in the “Web” tab of Google Search, but Google has since been testing it in the main “All” tab for some users.
Instead of a single ranked list, for a query like “best hiking trails in Colorado” you might see:
1. An AI-powered introduction to Colorado hiking
2. A categorized section for “Comprehensive Trail Guides” with links to relevant guides
3. Another section for “Easy Hiking Trails” supported by different links
3. A module on “Community Recommendations” filled with relevant Reddit discussions, and dynamic quote blocks
4. A reviews block on “Top-Rated Hikes by Locals & Visitors”
The idea is that for complex, exploratory searches, a flat list of results isn’t always the best way to find what you need.
Web Guide serves up a mix of results grouped by the different angles, sub-topics, and intents it infers from your query.
Google describes its dynamic SERP as using AI to “intelligently organize the search results page, making it easier to find information and web pages.”
There are three core elements to Web Guide.
Query fan-out
Query fan-out is when an AI takes your single search query and breaks it into multiple related sub-queries to find a wider range of results.
Web Guide uses the AI query fan-out process to find information you might not otherwise discover in a standard SERP ¹ ² ³
Google’s own Web Guide announcement confirms this.
To be more specific, query fan-out is the mechanism that expands the user’s original search into multiple sub-queries, then groups the returning results into the themed clusters you see in Web Guide.
Here’s the step-by-step process:
- You enter a search query, like “best hiking trails in Colorado”
- Gemini’s custom model analyzes your query and finds sub-queries or related angles like “beginner hiking trails Colorado”, “challenging 14ers”, and “scenic hikes near Denver”—this is the “fan-out” part
- These sub-queries then get searched simultaneously
- Results from all sub-queries SERPs get collected and deduplicated, removing URLs that appear more than once
- Gemini organizes the results into topical clusters (sets of results that share a common sub-topic) and labels each one with a descriptive heading.
- The clustered results are displayed to you via Web Guide
Fan-out is the same underlying technique used in AI Mode and AI Overviews.
You can think of each block/header in Google’s Web Guide as a distinct group of fan-out results.
Personalization
Web Guide results are heavily personalized to the user’s data.
That’s according to Wordlift CEO, Andrea Volpini, who dug into the network traffic (HAR files), and found the fan-out process is shaped by personalization factors like the user’s:
- Search history (e.g. if you’ve recently searched for marathon training, Web Guide might prioritize fitness-related clusters for a broad query like “recovery tips”)
- Interests (e.g. a user who regularly reads photography content might see camera-specific clusters for “best travel gear”)
- Location (e.g. a search for “weekend hikes” in Denver will surface different results than the same query in London)
- Device (e.g. searching from an iPhone might surface different app or accessory recommendations than searching from a Chromebook)
“Web Guide represents a shift away from a web of pages and average rankings and toward a web of understanding and hyper-personalization. By casting this wider net, the AI gathers a richer, more diverse set of results. It then analyzes and organizes these results into the thematic clusters presented to the user. This is the engine of hyper-personalization. I personally don’t see Web Guide as just another feature; I see it as a glimpse into the future of how knowledge shall be discovered and consumed.”
FastSearch
Web Guide often serves up “Quick matches” at the top of the SERP. These are plain blue, un-themed organic links.
According to Dr. Pete Myers over at Moz, these kinds of results run on FastSearch—a lightweight, streamlined retrieval system.
Instead of querying Google’s full index, FastSearch uses RankEmbed—a deep-learning model—to return semantically relevant results in a matter of milliseconds.
Fun fact
FastSearch is also the same underlying technology used to power AI Overviews and AI Mode.
So, the system powering Web Guide values efficiency and clarity. This is an important thing for SEOs to know.
Bloated, poorly structured content will struggle to make the cut.
To rank in certain Web Guide results, your content needs to be an easy “yes” for the system, since it doesn’t have time to dig.
Web Guide is currently available as an opt-in experiment through Google Search Labs. Here’s how to enable it:
1. Sign into your Google account.
2. Go to Google Search Labs (labs.google.com/search) and click “Web Guide”
3. Find the “Web Guide” experiment and toggle it on.
4. Search as normal—Web Guide results will appear in the Web tab.
As of March 2026, Web Guide is available in the US, with Google expanding to additional markets.
Search Labs experiments can be retired or graduated to the main product at any time.
Web Guide’s long-term status isn’t confirmed, but Google has publicly said it received positive user feedback and has been expanding the experiment to cover more query types.
Soon, Google may have three official AI search experiences: Web Guide, AI Overviews, and AI Mode.
Here’s how they compare.
| Feature | Web Guide | AI Overviews | AI Mode | Traditional search |
|---|---|---|---|---|
| What it shows | Clustered web links under themed headings | AI-written summary with inline citations | Conversational AI response with cited sources | Flat list of 10 blue links |
| Users click to websites? | Yes, all results are clickable links | Rarely. Full answer is provided on SERP | Rarely. Full answer is provided on SERP | Yes |
| AI generates text? | Yes, but only very short header intros | Yes, writes a summary | Yes, full conversational answer | No |
| Uses query fan-out? | Yes, to group results by sub-topic | Yes, for citation gathering | Yes, for deep research queries | No |
| Best for | Exploratory, open-ended queries | Quick factual answers | Deep research, follow-ups | Direct, navigational queries |
Web Guide may actually improve click-through rates
Web Guide is the most “website-friendly” of the three AI features because every result is a clickable link.
Unlike AI Overviews and AI Mode, which can satisfy queries without a click, Web Guide lays out the SERP in magazine-style segments supported by link cards and multimedia content.
While there are supporting AI summaries, users still have to click through for the meat of the content.
On the flip side, our research shows that AI Overviews suppress clicks by ~58%.
And a Pew Research study found only 8% of searches result in a click when AI Overviews appear, compared to 15% without them.
Web Guide sidesteps that zero-click problem entirely.
That said, it currently only appears for specific query types (e.g. exploratory: “things to do in Tokyo”, complex: “best approach to training for a triathlon as a beginner”, and open-ended: “what should I know before starting a business?”).
It’s not replacing traditional search for navigational or simple factual queries, so the CTR impact will be query-dependent.
Will Web Guide overtake AI Overviews or AI Mode?
It’s too early to say, but Web Guide’s biggest advantage is that it’s easier for Google to monetize.
Traditionally, a user searches, sees ads alongside results, clicks through to a site, and the advertiser pays.
But AI Overviews and AI Mode satisfy user intent directly on the SERP, which kills the click that Google’s entire ad model depends on.
Last year, AI Overviews appeared on only 5.5% of commercial intent queries, according to our research.
But a recent study of 20.9M shopping SERPs by Jeff Oxford, CEO of Visibility Labs, found that AIOs now appear on 14% of commercial shopping queries (up 5.6x in four months).
Adding to that, ads appearing alongside AI Overviews rose from roughly 3% in January to 40% by November 2025.
Google is aggressively ramping up its monetization—but, at the same time, it’s cannibalizing its highest-value ad inventory.
Web Guide is a straightforward solution to that problem. It serves every result as a clickable link, which keeps ad opportunity intact.
Another advantage Web Guide has over AI Overviews and AI Mode is it’s an “AI-lite” solution that’s cheaper to run.
It uses AI to organize and label results, not to generate long-form answers, which means it has a significantly lower compute cost.
If Google’s ad revenue drops or AI compute costs eat into margins, that could make Web Guide the more favorable format long-term.
“I really like the experience. I think Web Guide + Gemini will be the survivors. More than likely, the default + AI Mode will go away. It’s sort of what [Google] always did with fractured intent, which is to show a mix of results, but now it’s actually organized and basically displays an AIO for each section.”
Optimizing for Web Guide comes down to two things: covering topics comprehensively and structuring your content clearly.
Build topical clusters, not isolated pages
In traditional search, position one gets the most clicks and page two is where we bury the bodies.
But in Web Guide, a specialized page covering one niche angle could earn its place in a curated block, even if it wouldn’t crack the top 10 in a flat SERP.
Smaller, specialized sites have more skin in the game here.
If you’ve written the definitive page about one narrow angle of a topic, Web Guide may give you the chance to win visibility over larger, more authoritative domains.
For example, below we’ve searched for the fairly esoteric product: “Purple laser pointer”.
Numerous niche sites show up in this SERP.
And that also includes our very own Product Advisor, Patrick Stox, with an old experiment page on his site.
You can give yourself the best possible chance of visibility by building topic clusters.
Create a hub page for your main topic, plus supporting articles that cover specific sub-topics in depth.
Say you’re targeting “injury types”. Instead of writing one huge, generic post, create dedicated pages for “rotator cuff injury”, “plantar fasciitis”, “ACL tear”—get more granular as you go.
When someone performs an injury-related search and Web Guide fans out the query, each of those sub-topics could feasibly become its own cluster.
If you have a page for each, you’re more likely to appear across multiple clusters for a single search.
Find related topic ideas with Parent Topics
Parent Topics in Ahrefs Keywords Explorer will show you how niche search topics ladder up to bigger pillars.
Use it to map out different angles and sub-topics to build out your cluster content.
Create content around Gemini’s fan-out topics
Since query fan-out breaks a topic into sub-topics, sites that cover those sub-topics comprehensively are more likely to appear across multiple headers in Google Web Guide results.
The thing is, Google doesn’t expose the fan-out queries Gemini generates.
But they are predictable and tend to align with the sub-topics and questions people already search for.
To find fan-out query ideas, enter your target keyword in Ahrefs’ Keywords Explorer and open the Matching terms report.
Then hit the Questions report tab: it surfaces the specific questions searchers ask about your topic, which may map to the different header sections Web Guide curates.
In Brand Radar you can also see thousands of fan-outs generated by ChatGPT and Perplexity to get an idea of how Gemini’s custom model might similarly expand a long-tail query.
It doesn’t matter whether you manage to perfectly mirror Gemini’s internal fan-out queries.
It’s more important that you analyze lots of topics and questions at scale, so you can spot the recurring themes and intent angles that Gemini may consider when constructing Web Guide results.
SEO experts Mike King and Dr. Pete Meyers have both attempted to categorize the different types of fan-out query.
Mike King based his categorization on official patents that indicate Google’s approach to constructing synthetic fan-out queries.
Dr. Pete Myers has taken a slightly different tack, studying numerous Web Guide headers to build a framework that matches Gemini’s query expansion.
Their classifications include categories like:
- Comparative queries (e.g. “What’s more durable; the Dogma F or the Cervélo S5?”)
- Personalized queries (e.g. “Dogma F road bike near me”)
- Attribute queries (e.g. “Does disc brakes vs rim brakes affect aerodynamics?”)
- Tutorial queries (e.g. “How to replace bar tape on an integrated handlebar”)
- Entity queries (e.g. “Are Canyon bikes good value for the money?”)
You can use these as a starting point for your own research.
In Ahrefs Keywords Explorer, apply filters to surface keywords that map to these fan-out types.
For example, adding a presets filter in the Matching Terms report pulls up comparative topics.
Or adding an includes filter for the key phrases “how to” or “guide” can help you map to tutorial fan-outs, while modifiers like “near me” can help you find personalization-style queries.
The goal is to make sure you’re covering the types of sub-topics and questions Gemini is likely generating behind the scenes.
Use clear, descriptive headings
Gemini needs to quickly categorize the sub-topic your page covers.
Pages with well-structured content and specific headings like “How email deliverability affects open rates” are easier to categorize than pages with vague headers like “Key takeaways” or “Things to consider.”
Structure your articles with H2 and H3 tags that describe the specific angle each section covers.
You can audit missing H2 tags in the Page Explorer report in Site Audit, with the following filter.
Alternatively, you can check your heading structure using Ahrefs SEO Toolbar while visiting your content.
Or just drop your article into Ahrefs AI Content Helper and check the “Headings” tab—it’ll show you recommendations alongside the heading structure of your top competitors.
Build strong internal links
Links are crucial for Web Guide visibility. They drive the rankings that put you in Gemini’s consideration pool during fan-out.
You specifically need to link your supporting articles back to your hub page and to each other. This signals to Google (and Gemini) that your pages form a cohesive topic cluster.
Use the Internal Link Opportunities report in Site Audit to build stronger associations between each page.
It works by looking at the top 10 keywords for your page and finding mentions of them on other pages, giving you ready-made link recommendations.
If your “email deliverability” article links to your “email marketing” hub and your “email subject lines” article, that’s a signal to Gemini that all three belong to the same topical cluster.
Study intent types using Identify Intents in Keywords Explorer
You can get an idea of the kinds of heading types that are most likely to generate in a Web Guide SERP, using our Identify Intents tool.
To pull it up, just search your keyword in Ahrefs Keywords Explorer.
Then scroll down, and hit “Identify Intents”.
For the query we explored earlier—“best hiking trails in Colorado”—Identify Intents picks up on some of the same header categories that turned up in Web Guide, including location based hiking trails, and community recommendations.
It goes without saying that the traditional SERP results are more general than Web Guide—which is why 58% of the pages you see above are broad “best of” lists.
You won’t find a like-for-like intent match.
But there are still useful intent clues hiding in the traditional SERP that you can design your cluster content around, if you want to show up in Web Guide.
There’s no dedicated Web Guide tracking tool yet. But you can monitor the signals that indicate Web Guide visibility.
Set up a keyword list in Ahrefs’ Rank Tracker that includes your head term plus all the sub-topic keywords you identified during research.
If you begin ranking for queries across multiple related sub-topics, you may be appearing in Web Guide clusters.
The Share of Voice metric is especially useful here—it shows what percentage of total search visibility your site captures across your full keyword set.
Also, watch Web Analytics or Google Search Console for impression/view and click/visit changes on sub-topic pages.
Web Guide may surface pages that weren’t previously getting impressions for certain queries.
An unexpected bump in impressions on a niche or supporting article could signal Web Guide inclusion.
You can also use Ahrefs’ Brand Radar to monitor how AI features cite your content.
While Brand Radar tracks AI Overviews and AI Mode rather than Web Guide specifically, shifts in AI citation patterns can indicate whether Gemini is surfacing your pages more or less frequently.
As Web Guide matures and potentially graduates from Search Labs to a full product, SEO platforms will likely add dedicated tracking.
We may even see that data broken out in Google Analytics or Search Console.
Google has historically been secretive about data from low-click AI surfaces, bucketing it in with organic search data—but I expect it could be a different story with Web Guide.
After all, it’s a SERP that actively encourages clicks, so there’s less reason to hide the numbers.
We’ll just have to wait and see!
Final thoughts
For now Web Guide is an experiment, but it might be Google’s quiet solution to a few increasingly loud problems, like shrinking ad clicks and rising AI compute costs.
It also signals where Google Search may be heading: from rankings to curation.
Like a magazine editor, Google is deciding what’s relevant, and how information should be grouped, sequenced, and framed for users.
The sites that think like editors—building defined, well-structured topic coverage—are the ones Gemini will have an easy time surfacing.
Got questions? Ping me on LinkedIn.
