I’m excited to share that Microsoft has launched a groundbreaking search service specifically designed for AI-agents, as agents have unique search requirements compared to humans.
I’ve learned that Microsoft’s latest innovation, Web IQ, is here to bridge AI systems with real-time intelligence online. As a suite of AI-native grounding APIs, Web IQ sources fresh data, be it web pages, news, images, or videos, as announced by Microsoft here.
What is Web IQ? Web IQ is all about connecting AI systems to real-world updates, leveraging Bing’s index for superior understanding. I find it fascinating how it uses the same infrastructure as Microsoft Copilot and other leading LLMs, like ChatGPT.
However, I discovered that Web IQ’s APIs are newly developed for efficiency and relevance, crucial for serving Bing, Copilot, and ChatGPT queries rapidly.
For AI-Agents, Not Humans. Web IQ tailors search results specifically for AI-agents. Unlike human-oriented Bing Search, ranking isn’t a priority here, as agents need swift information extraction, as stated by Jordi Ribas, President of Search & AI at Microsoft.
Unlike us, AI-agents don’t just issue a single query; they delve deeper and continuously expand their search. This paradigm shift meant re-architecting search from indexing to orchestration, aligning it with AI needs, as per Microsoft’s insights.
Given the frequency of searches AI-agents perform, Microsoft designed Web IQ to operate efficiently, minimizing token usage to deliver better and faster results. It’s currently 2.5 times faster than its nearest competitor.
Access and Availability. At present, Web IQ supports Microsoft Copilot, OpenAI’s ChatGPT, and other large LLM platforms. As Microsoft scales this technology, I expect wider access to follow.
If you want to express interest in Web IQ, Microsoft encourages you to visit this page.
Why this Matters. As we witness the web transforming to accommodate agentic technologies, keeping an eye on these developments is vital. Websites, including mine, must evolve alongside these AI advancements.
AI-agents aren’t just a trend; they’re part of the web’s next evolution. I’m preparing to embrace this change, and I suggest you do too.
As I immerse myself in Google’s latest guidance on AI search optimization, it’s hard not to approach it with a healthy dose of skepticism.
Whenever Google releases a new Search Central document, our industry splits into two predictable groups. The first group eagerly screenshots the content to share on LinkedIn, captioning it with “SEE? IT’S JUST SEO” before returning to their usual practices. In contrast, the second camp underscores their posts with, “Here’s proof they’re deceiving us,” treating Google’s words as gospel as long as it supports their pre-existing beliefs.
Recently, Google updated its guide on optimizing websites for generative AI features. The “it’s just SEO” advocates had much to celebrate. Many emerging concepts were downplayed or outright dismissed by the guide, reinforcing their belief that not much has changed over the years.
Yet, I can’t help but recall the critical insight we gained a couple of years back from leaked internal documents. Those leaked papers revealed discrepancies between Google’s public messages and what their internal documentation actually detailed. Despite public denials, these documents showed certain signals were very much a part of Google’s algorithms. This reinforces the need for caution in taking Google’s public directions at face value.
I’m not suggesting everything in Google’s new guidance is misleading, but it’s important to note Google’s tendency to push the industry towards its own interests first, possibly benefitting the open web as an afterthought. Google’s narrative drives SEOs to maintain the web’s infrastructure rather than moving towards a more independent approach across diverse platforms.
In my previous discussions about chunking, I’ve highlighted how Google’s influence is waning, as competitive AI platforms redirect user attention. Google’s once-dominant definition of “good content” is now challenged, as evident in their increasingly protective language.
Meanwhile, over at Microsoft, Bing is taking a different approach, transparent about changes and offering publishers insights and tools to optimize their content’s performance in AI responses.
For instance, in their posts, Bing describes the transition towards Generative Engine Optimization and provides practical tools for users, something Google hasn’t quite matched.
So, let’s discuss Google’s claims point by point:
“Is SEO still relevant for generative AI search?”
The idea that “it’s just SEO” is overly simplistic. SEO encompasses more than a collection of tactics; it includes strategic thinking and organizational presence. SEO has been evolving beyond basic practices to influence broader content strategies, yet it is often still underestimated as a supportive task.
This pattern has persisted across various developments, from mobile and voice search to schema and AMP, all initially labeled as merely “SEO.” Each innovation triggers more work for SEO professionals without an equivalent increase in resources.
The skill set and audience have diversified. Traditional SEO targets machine and human users differently than AI Search, which also caters to systems that might bypass traditional site visits altogether.
New labels, like AEO and GEO, can prioritize budgets and attention towards such progressive approaches, unlike the catch-all label of SEO.
When AI Search is recognized distinctly within organizations, it can catalyze cross-functional collaboration and sponsorships that SEOs have long sought.
Despite the extra responsibility placed on practitioners, aligning AI Search under the SEO umbrella usually doesn’t come with new resources or authority, which limits growth and innovation.
Google’s approach, treating all work as “just SEO” rather than recognizing unique systems like AI Mode or AI Overviews, simplifies the real diversity within their technologies.
Non-commodity content is key. Creating valuable and unique content is universally acknowledged as a good practice.
llms.txt files are beneficial, even if Google doesn’t require them. They serve other systems and therefore should be considered in a broad strategy.
Ignoring the multi-platform dynamics leaves a business vulnerable to losing ground where other systems are gaining traction.
Understanding that Google’s public guidance is tailored to its interests rather than offering generalized best practices across all platforms is crucial for developing a robust SEO strategy in this new era.
Google’s recommendations are one perspective in a rapidly evolving landscape where multiple opinions and infrastructures are emerging.
Stay informed, apply what’s relevant, but don’t take any single source as absolute truth. We’re navigating a new world requiring attention to diverse strategies to succeed across platforms.
First published on the iPullRank blog, republished here with permission.
I’ve noticed that Bing is testing a double-rowed sponsored product carousel in its shopping results. As someone who keeps an eye on these updates, this change could offer substantial visibility boosts for Microsoft Shopping advertisers.
The test, first spotted by Digital Marketer Sachin Patel, caught my attention when he noticed the broader layout while searching for cushions on Bing. This new format combines a significant double-rowed sponsored carousel, prominently paired with organic results below.
Why this matters to me: If Bing decides to roll out this format broadly, I foresee a significant increase in screen space dedicated to sponsored products. This extra visibility typically translates to higher click-through rates, especially for those running Microsoft Shopping campaigns. The visually appealing double-row carousel puts Bing’s shopping ads on par with similar offerings by Google Shopping.
Here’s the catch: The test seems to be in its early stages, as not all users, including seasoned industry experts like Mordy Oberstein, are seeing this expanded format. When I checked myself, I noticed a more compact layout, hinting at Bing’s ongoing experimentation.
The takeaway: Bing often experiments with its search engine results pages without officially rolling them out. As a retailer using Microsoft Shopping, it’s crucial for me to stay alert for any increase in product impressions if the format becomes more widespread.
Initially discovered. This testing phase was initially spotted by Sachin Paten, who shared his insights and a screenshot on X.
Recently, I’ve noticed something exciting happening on Bing. Now, when I use Bing Webmaster Tools, I can click a query to view its cited pages or select a page to see its grounding queries. It feels like a new level of connectivity where multiple queries and pages are seamlessly linked together.
Microsoft has introduced query-to-page mapping within its AI Performance report on Bing Webmaster Tools. I find this feature incredibly helpful because it lets me directly connect AI-generated queries to cited URLs. This makes my SEO strategies more precise.
Why it matters to us. Before this update, Bing’s dashboard presented queries and pages separately, which limited our optimization efforts. Now, I can align specific AI-triggering queries with the exact pages they reference, focusing my updates on real AI-driven demand rather than guesswork.
Here’s the scoop. The Grounding Query–Page Mapping feature is a game-changer in the AI Performance dashboard:
With a click on a grounding query, I can see which pages are cited.
I can also click a page to find out which grounding queries are driving its citations.
The mapping system is many-to-many, meaning one query can be linked to multiple pages and vice versa.
Catch up with Bing. Back in February, Microsoft launched the AI Performance report in Bing Webmaster Tools, marking its initial GEO-focused dashboard. This tool keeps track of where and how often my content gets cited in AI answers across platforms like Bing, Copilot, and more.
It tracks the grounding queries, cited URLs, and visibility trends over time, providing an insightful view into citation visibility.
The buzz. According to Microsoft, this update came about due to “strong positive customer feedback and numerous requests,” and I can see why it’s so well-received.
I’ve discovered that Microsoft Advertising is rolling out a captivating new feature that could transform how we see Shopping campaigns in Bing search results. These multi-image ads offer eCommerce brands a unique opportunity to showcase their products more vividly, potentially capturing shopper attention even before they click.
What’s new. Now, I can include multiple product images in a single Shopping ad, allowing shoppers to preview various angles, styles, or variations directly within the search results. This approach could be a game-changer for advertisers.
The design is crafted to enhance visual engagement and provide more informative ads. It allows consumers like myself to quickly compare options without the need to leave the results page.
How it works:
I can upload additional images using the optional additional_image_link attribute in the product feed.
There is an option to include up to 10 images, which I can separate by commas.
The images will appear alongside pricing and retailer information in Shopping results.
Why we care. From my perspective, multi-image ads have the potential to boost engagement and purchase intent by offering a more comprehensive visual representation of a product. More imagery can highlight features, colors, and design elements that a single image might miss.
Discovery. This feature was initially noticed by digital marketer Arpan Banerjee, who shared it on LinkedIn.
The bottom line. For retailers like you and me, multi-image Shopping ads provide more creative freedom and give shoppers a richer context immediately. This shift has the potential to enhance ad performance and reshape how products are presented in search results.
Today, I stumbled upon some exciting news from Microsoft. They have officially launched the AI Performance feature in Bing Webmaster Tools, albeit in beta. Now, I have a tool that lets me see where and how often my content is cited in AI-generated answers across platforms like Microsoft Copilot and Bing’s AI summaries.
What I find particularly useful is how AI Performance details exactly which URLs from my website are cited, the queries that trigger those citations, and how this activity evolves over time. It feels like a game-changer for understanding my content’s footprint in the AI domain.
Initially, Search Engine Land reported on January 27 that Microsoft was testing the AI Performance report. Today, I can tell you firsthand that this new dashboard in Bing Webmaster Tools is a treasure trove for tracking citation visibility across AI interfaces.
What’s new? I now have access to a specific dashboard dedicated to AI Performance. Unlike typical SEO tools that measure clicks or rankings, this one reveals if my content is grounding AI-generated answers. Microsoft describes it as an early step toward Generative Engine Optimization (GEO), helping me comprehend how my work appears in AI-oriented discovery.
What it looks like? Thanks to Microsoft, I’ve seen an image of the AI Performance feature in action. It’s sleek and provides clear insights into how my content is performing across AI experiences.
Insights from the dashboard? The AI Performance dashboard offers several new metrics, which include:
Total citations: This tells me how many times my site is used as a source for AI-generated answers over a set period.
Average cited pages: This metric gives me the average number of unique URLs from my site that AI systems reference daily.
Grounding queries: These are sample query phrases that AI systems utilize to retrieve and cite my content.
Page-level citation activity: Showing citation counts by URL, it highlights which pages of mine are popular in AI responses.
Visibility trends over time: I can see a timeline view that shows how citation activity changes throughout different AI platforms.
Though these metrics are informative, they only reflect citation frequency. They don’t give insights into my content’s ranking, prominence, or its specific contribution to AI answers. That’s something I’d have to explore further.
Why I care? Knowing where and how my content is cited is fantastic, yet Bing Webmaster Tools doesn’t yet show how these citations convert into clicks, traffic, or concrete business results. Without click data, it’s still an open question whether AI visibility provides actual value.
How can I use this? Microsoft suggests I utilize this data to:
– Verify which pages of mine already appear in AI answers.
– Spot topics that frequently show up across AI-generated responses.
– Enhance clarity, structure, and completeness on less frequently cited pages.
The advice echoes familiar best practices: maintaining clear headings, evidence-backed claims, up-to-date information, and consistent entity representation.
What comes next? Microsoft has promised improvements in inclusion, attribution, and visibility across both search results and AI experiences, and to keep evolving these capabilities moving forward.
I’ve been following the lively debate around creating separate markdown pages for LLMs, and it appears that both Google and Bing are advising against this approach.
Recently, I noticed that representatives from Google Search and Bing Search have specifically recommended not to create separate markdown (.md) pages designed exclusively for LLMs. This practice involves presenting different content to the LLMs compared to what users see, which can be considered a form of cloaking—a direct violation of Google’s policies.
The question arose when Lily Ray inquired on Bluesky about the prevalence of creating markdown or JSON pages targeted at bots.
“Not sure if you can answer, but starting to hear a lot about creating separate markdown / JSON pages for LLMs and serving those URLs to bots.”
Google’s stance, as explained by John Mueller, is clear. He replied to Lily’s query saying that LLMs have always interacted with standard web pages and don’t require separate markdown pages.
“I’m not aware of anything in that regard. In my POV, LLMs have trained on—read & parsed—normal web pages since the beginning, it seems a given that they have no problems dealing with HTML. Why would they want to see a page that no user sees? And, if they check for equivalence, why not use HTML?”
John Mueller even criticized the whole idea, stating:
“Converting pages to markdown is such a stupid idea. Did you know LLMs can read images? WHY NOT TURN YOUR WHOLE SITE INTO AN IMAGE?” Of course, converting your entire site to a markdown format is an extreme measure.
I’ve collected many of John Mueller’s remarks on this topic, which you can find here.
Bing’s perspective is shared by Fabrice Canel from Microsoft Bing, who suggested that creating duplicate, non-user content isn’t effective.
“Lily: really want to double crawl load? We’ll crawl anyway to check similarity. Non-user versions (crawlable AJAX and like) are often neglected, broken. Humans eyes help fixing people and bot-viewed content. We like Schema in pages. AI makes us great at understanding web pages. Less is more in SEO!”
Why this matters to us: Many of us are tempted by shortcuts to improve search engine performance. Yet, these shortcuts often backfire or yield short-lived benefits. As Lily Ray remarked on LinkedIn, managing duplicate and differing content for bots violates established search engine policies.
“I’ve had concerns the entire time about managing duplicate content and serving different content to crawlers than to humans, which I understand might be useful for AI search but directly violates search engines’ longstanding policies about this (basically cloaking).”
I’m excited to share that Microsoft has introduced a game-changing update to Bing with the global rollout of multi-turn search. As I scroll through Bing’s search results, I now see a Copilot search box conveniently positioned at the bottom, waiting to assist with follow-up queries.
What is multi-turn search? In essence, this feature enables me to continue my search seamlessly. Imagine typing a follow-up question in the Copilot search box right at the bottom of the results page without any need to scroll back up. It feels so intuitive and user-friendly!
Here’s a vivid screenshot that perfectly captures this experience:
And here’s a video that brings it to life, showcasing the seamless functionality:
Here’s what Microsoft had to say. Jordi Ribas, the CVP and Head of Search at Microsoft, took to X to share this exciting update, revealing that “After shipping in the US last year, multi-turn search in Bing is now available worldwide.”
Ribas went on to explain that “Bing users don’t need to scroll up to do the next query, and the next turn will keep context when appropriate,” indicating a significant enhancement in user experience.
He further noted, “We’ve seen gains in engagement and sessions per user in our online metrics, highlighting the positive user value of this approach.”
Why it’s important for us. With many search engines, including giants like Google, trying to push for more AI integration, Bing’s new feature is a step in that direction. Google’s AI Overviews, although not entirely without controversy, are pushing users deeper into AI interfaces. Meanwhile, Bing’s Copilot box, after rigorous testing over several months, is now fully available, underscoring Microsoft’s commitment to user-centered innovation.
I recently discovered that Bing is testing a new AI Performance report within their Webmaster Tools. This has piqued my interest, especially since Microsoft has been teasing the idea of providing better insights into website performance in AI-driven Bing and Copilot searches for months.
It all started back in February 2023, and then in April 2023, Microsoft hinted at delivering data on Bing Chat and AI search impressions. Sadly, our hopes were dashed when they lumped this data together with regular web queries, leaving us still in the dark about our sites’ performance in Bing’s AI experiences. I can’t help but feel a bit let down.
Now, it seems Bing is experimenting with a new report within Bing Webmaster Tools, known as the AI Performance report. This report is in a super limited beta phase, and Microsoft hasn’t officially announced anything yet. A source shared that it showcases citation data from both Microsoft Copilot and its partners, detailing the number of citations and cited pages per day.
With this report, I can see how often Copilot cites my website and across how many pages. However, it still doesn’t reveal how many people clicked through from those citations to my site. The report also presents data categorized by “grounding queries” and “pages.” While “grounding queries” might not represent the exact query entered in Copilot, it shows how Bing interprets them, including insights into the intent behind such queries, like whether they are navigational or informational.
This new report lets me identify the specific pages Copilot cites. While there’s excitement in seeing more AI performance-related data pop up in Bing Webmaster Tools, I can’t shake the feeling of wanting click-through data. Knowing the click-through rate from AI interactions compared to regular web searches is something I, and I’m sure many other publishers and site owners, have been eagerly anticipating.
It feels like all search engines are intentionally keeping this data under wraps, and it’s frustrating not having full transparency.
Have you ever considered how duplicate content might be impacting your visibility in AI search results? Fabrice Canel and Krishna Madhavan from Microsoft recently discussed how duplicate content complicates AI search systems, reducing the chances of selecting the correct version for summarization.
Much like traditional search engines, AI search platforms such as Bing and Google rely on consistent intent signals. When your content appears in duplicate forms, it can confuse these systems, making it difficult for them to interpret signals accurately.
The Impact of Duplicate Content on AI Search. Here are key takeaways from the Bing blog about the impact of duplicate content:
AI search utilizes traditional SEO signals while also adding layers to understand user intent.
Repeated content across multiple pages weakens intent signals, complicating AI interpretation, and selection.
If several pages contain similar content, AI cannot easily identify which aligns with user intent, reducing preferred page selection chances.
Large Language Models (LLMs) cluster near-duplicate URLs, often selecting outdated versions if variations are minimal.
Campaign pages and localized versions must differ meaningfully; identical content provides less matching signal.
AI favors updates, but duplicates can slow the process of updating system information.
The Challenge of Syndicated Content. Many might not realize syndicated content—articles republished on various sites—can also be problematic. Microsoft considers this duplicate content because identical articles across domains make it difficult for search engines and AI to identify the original source.
Strategies to Minimize Duplicate Content. If you deal with syndicated content, ask partners to:
Use canonical tags directing to the original version on your site.
Rework content for uniqueness.
Noindex republished articles to prevent search engine indexing.
Organizing Campaign Pages for Clarity. Microsoft warns that campaign pages with only minor changes can still be considered duplicates. To manage this:
Designate a primary campaign page for interaction.
Apply canonical tags to variations without unique intent.
Maintain separate pages for distinct intents like seasonal offers or local pricing.
Redirect outdated or redundant pages to consolidate content.
Handling Localization Pages. Localization can also produce duplicate content if differences are minimal. Microsoft suggests:
Introduce meaningful local variations with examples, terminology, or regulations.
Avoid multiple same-language pages for identical purposes.
Use hreflang to define language and regional targeting accurately.
Addressing Technical SEO Concerns. Technical issues can lead to URL duplication, often managed automatically by search engines. However, it’s best to prevent this by maintaining a single URL per content piece. Common problems include:
Utilize 301 redirects for URL consolidation.
Apply canonical tags when accessible versions are necessary.
Ensure consistent URL structures site-wide.
Restrict crawler access to staging or archived URLs.
Why This Matters. While duplicate content is not a new issue in SEO, its importance extends into AI search. Familiarity with its impact on indexing and ranking can guide strategies for improved visibility.
