Search is no longer only a list of links.

Connect this guide to SEO in the AI-search era, the SEO and GEO differences, the Google AI Overview optimization playbook, and the framework to measure AI-search visibility and ROI.

A customer can now ask Google AI Mode to compare options, ask ChatGPT to research a category, use Perplexity to investigate a problem, or use Copilot to synthesize several sources before ever visiting a company’s website.

That changes the job of search visibility.

The question is no longer only:

Can your page rank?

It is also:

Can an answer system discover your information, understand it correctly, retrieve the right passage, trust the supporting evidence, and present your company accurately when a customer asks a relevant question?

That is the practical job of Answer Engine Optimization.

The problem is that “AEO” has also become a magnet for shortcuts.

In 2026 you can find recommendations to add special AI schema, rewrite every page into tiny content chunks, manufacture brand mentions, create dozens of near-identical question pages, or treat an llms.txt file as a ranking lever.

For Google Search, Google has explicitly said those are not special requirements for AI Overviews or AI Mode. Its current guidance says the familiar SEO foundations still apply, there is no special schema.org markup required for its generative AI features, and publishers do not need a new AI text file to participate. [1][2]

So a useful AEO strategy has to separate two things:

  1. What the platforms actually document and let publishers control
  2. What marketers have inferred from experiments, correlations, or third-party tools

This guide does that.

What Is Answer Engine Optimization?

There is no single cross-platform technical standard called “AEO.”

WKND uses the term operationally:

Answer Engine Optimization is the process of improving the discoverability, retrievability, clarity, authority, and measurability of a brand’s information across answer-driven search and AI experiences.

That includes surfaces such as:

  • Google AI Overviews
  • Google AI Mode
  • ChatGPT Search
  • Perplexity
  • Microsoft Copilot and Bing AI experiences
  • other systems that retrieve or summarize public web information

The important phrase is answer-driven.

Traditional SEO often optimizes for a ranked result that a user chooses to click.

AEO also accounts for environments where the system may synthesize an answer before the user decides whether to click.

That does not mean SEO disappeared.

For Google specifically, the opposite is true. Google says AI Overviews and AI Mode are rooted in its core Search ranking and quality systems. A page must be indexed and eligible to appear in Search with a snippet before it can be eligible as a supporting link in those generative experiences. [1][2]

So the relationship is better expressed as:

SEO creates search eligibility and authority.
AEO extends that work into answer-driven discovery, retrieval, representation, and measurement.

AEO vs SEO: What Is Actually Different?

The internet often frames AEO and SEO as two opposing disciplines.

That is too simplistic.

AEO and SEO overlap heavily in the parts that make information discoverable and trustworthy:

  • crawlability
  • indexation
  • helpful content
  • internal linking
  • clear page structure
  • authority
  • accurate business information
  • useful original evidence

Where AEO adds work is in the answer layer.

AEO forces teams to ask questions such as:

  • What questions are customers asking instead of only what keywords are they typing?
  • Which systems can access our content?
  • Is our company described consistently enough to be represented correctly?
  • Can a useful answer be extracted without stripping away important context?
  • Do our claims have evidence?
  • Are we visible across answer surfaces even when no click occurs?
  • Can we measure citations, mentions, AI-search impressions, referrals, and downstream conversions?

AEO therefore changes the operating model more than it changes the basic technical foundation.

What AEO does not replace

AEO does not replace:

  • technical SEO
  • content strategy
  • site architecture
  • internal linking
  • digital PR
  • brand building
  • analytics
  • conversion optimization

It makes those systems work in a search environment where the answer may appear before the visit.

The First Rule of AEO in 2026: Stop Looking for One Universal Ranking Formula

Google, ChatGPT Search, Perplexity, and Copilot are not one system.

They do not publish one shared ranking formula.

They do not use identical crawlers.

They do not expose identical reporting.

They do not even define “visibility” in exactly the same way.

That means a serious AEO program should start with platform scope.

For every platform you care about, document:

  1. How your content can be discovered
  2. Which crawler or index controls are published
  3. Which webmaster or publisher controls exist
  4. Which visibility metrics are available
  5. Which claims are confirmed by first-party documentation
  6. Which tactics remain hypotheses that require testing

This prevents a common mistake: taking a tactic observed on one system and presenting it as a universal law.

Step 1: Define the Questions You Actually Want to Own

AEO begins before content optimization.

Start with a question set.

Not 5,000 random prompts.

Not a list of every keyword containing your industry name.

Build a deliberate panel representing the customer journey.

For example, a digital marketing agency might track questions such as:

Problem awareness

  • Why is my organic traffic declining even though rankings look stable?
  • How has AI search changed SEO?
  • Is traditional SEO still important after AI Mode?

Solution education

  • What is Answer Engine Optimization?
  • How does AEO work?
  • What is the difference between SEO, AEO, and GEO?

Evaluation

  • What should an AEO agency actually do?
  • How do I measure AI-search visibility?
  • Is Answer Engine Optimization worth paying for?

Purchase intent

  • Which AEO agency should I hire?
  • What does AEO cost?
  • How long does an AEO engagement take?

This question system becomes the unit you research, map, publish against, and measure.

It also prevents content cannibalization.

If three questions share the same underlying intent, they usually belong on one strong page rather than three weak pages.

Step 2: Establish a Baseline Before You Optimize Anything

You cannot prove improvement without a before state.

Create an AEO baseline that includes:

  • current traditional rankings
  • Search Console impressions and clicks
  • generative AI impressions in Google Search Console
  • existing citations or references in Bing AI Performance
  • ChatGPT referral traffic
  • priority prompts and whether your brand appears
  • which sources are cited when your brand does not appear
  • competitor visibility for the same questions
  • accuracy of the information systems currently return about your company

Google’s dedicated generative AI Search Console reporting now gives publishers a separate view of impressions within features such as AI Overviews and AI Mode, and Google says those reports were rolled out worldwide by August 31, 2026. [5]

Microsoft introduced AI Performance in Bing Webmaster Tools in 2026 to show when publisher URLs are cited across Microsoft Copilot, AI-generated Bing summaries, and supported partner integrations. [10]

OpenAI says publishers who allow OAI-SearchBot can track ChatGPT referral traffic because ChatGPT search referral URLs include utm_source=chatgpt.com. [8]

Those are measurable platform signals.

They are more useful than claiming success because someone manually prompted an AI assistant once and saw your brand.

Step 3: Fix Technical Eligibility Before Rewriting Content

AEO cannot rescue content that answer systems cannot access.

For Google, the technical floor is familiar:

  • Googlebot must not be blocked
  • the page must return a working HTTP status
  • the page must contain indexable content
  • the page must meet Search policies
  • the page must be eligible to appear with a snippet to be used as a supporting link in AI Overviews or AI Mode [1][3]

Google also recommends:

  • discoverable internal links
  • important information available as text
  • strong page experience
  • structured data that matches visible page content
  • crawl access through robots.txt and hosting infrastructure [1]

Other platforms publish their own controls.

ChatGPT Search

OpenAI says any public website can appear in ChatGPT search. To help content be discovered, surfaced, cited, and linked, publishers should make sure they are not blocking OAI-SearchBot. OpenAI also distinguishes OAI-SearchBot from GPTBot, which is used for separate purposes related to potential model training. [8]

Perplexity

Perplexity says PerplexityBot is designed to surface and link websites in Perplexity search results and is not used to crawl content for foundation-model training. It recommends allowing PerplexityBot and permitting requests from its published IP ranges. It also documents a separate Perplexity-User agent for user-requested page access. [9]

Bing and Copilot

Microsoft continues to emphasize normal crawlability, sitemaps, accurate lastmod data, and IndexNow for keeping content current across Bing and AI-powered experiences. [11]

This gives AEO teams a concrete technical checklist:

  • Is the page public?
  • Is the right search crawler allowed?
  • Is a firewall or CDN blocking legitimate bots?
  • Is the content rendered accessibly?
  • Is the canonical correct?
  • Is the page indexed where indexing is required?
  • Is the sitemap current?
  • Are real content updates reflected accurately?
  • Are snippet and preview controls doing what you intended?

Fix these before debating paragraph length.

Step 4: Make Answers Clear Without Writing for Robots

AEO content should be easy to extract because it is easy for a person to understand.

That is different from artificially chopping every article into tiny “AI chunks.”

Google explicitly says there is no requirement to break content into small pieces for generative AI search. It also says publishers do not need to rewrite content in some special way exclusively for generative AI systems. [2]

A better structure is:

State the question

Use a heading that clearly frames the problem.

Answer it directly

Give the reader the answer before forcing them through 600 words of setup.

Explain why

Add the reasoning, mechanism, tradeoffs, or limitations.

Support the answer

Use original data, examples, screenshots, primary sources, or expert evidence where the topic requires proof.

Give the next action

Tell the reader what to do with the information.

For example:

Does AEO replace SEO?

No. AEO does not replace SEO. For Google, generative AI features still rely on core Search ranking and quality systems, and pages must be indexed and eligible for Search to appear as supporting links. AEO adds optimization for answer-oriented discovery, representation, and measurement on top of those foundations. [1][2]

That paragraph is answer-friendly because it is useful.

Not because it was cut to an arbitrary token count.

Step 5: Build Topic Architecture Around Real Customer Problems

Google says AI Overviews and AI Mode can use query fan-out, issuing multiple related searches across subtopics and data sources while developing an answer. [1]

That has an important content implication:

One customer question may trigger research across an entire topic network.

A business should therefore avoid creating one disconnected page per keyword variation.

Instead, build a connected set of resources.

For an AEO cluster, that might include:

  • the core AEO service page
  • an AEO implementation guide
  • SEO vs GEO
  • Google AI Overview optimization
  • AI-search measurement
  • AI-search budgeting
  • industry-specific AI visibility research

Each asset should own a different intent.

Then link them intentionally.

This gives search systems multiple relevant entry points while giving humans a logical path through the topic.

Step 6: Publish Information That Is Worth Reusing

Commodity information is easy for an answer engine to synthesize from someone else.

Original evidence is harder to replace.

Google’s current AI-search guidance emphasizes unique, valuable, non-commodity content. [2]

That can include:

  • original experiments
  • internal benchmarks
  • proprietary research
  • customer data you are permitted to publish
  • first-hand implementation examples
  • screenshots
  • process diagrams
  • decision frameworks
  • calculators
  • templates
  • source comparisons
  • documented failures
  • expert commentary grounded in experience

Consider the difference.

Generic content says:

“AEO is important because people use AI search.”

Useful content says:

“We tested 25 unbranded buyer questions across four answer engines for 30 days. Here is which source type each platform cited, which pages changed, where the brand was represented incorrectly, and which changes correlated with a visibility shift.”

The second asset creates something an answer engine and a human can actually use.

Step 7: Use Structured Data Correctly, Not Magically

This is one of the most important AEO corrections for 2026.

Does schema markup help with AEO?

Structured data can still be useful as part of normal SEO and content understanding, but Google says there is no special schema.org markup required for AI Overviews or AI Mode. [1][2]

Google recommends that any structured data you use match the visible content on the page. It also warns against overfocusing on structured data as a generative-AI tactic. [1][2]

That means:

Do

  • use supported schema types when they accurately describe visible content
  • validate your structured data
  • keep Organization, Product, Article, LocalBusiness, Breadcrumb, and other relevant markup accurate where appropriate
  • maintain consistency between the markup and what users actually see

Do not

  • add schema types just because someone calls them “AEO schema”
  • assume markup guarantees inclusion in AI answers
  • create invisible or misleading structured data
  • treat FAQ markup as a shortcut to Google’s generative AI features

There is another 2026 update businesses should know.

Google deprecated FAQ rich results in May 2026 and removed the FAQ rich-result documentation in June 2026. [4]

So an AEO plan built around “deploy FAQ schema everywhere” is already stale if it is presented as a Google AI visibility tactic.

FAQ sections can still be useful to readers.

Question-and-answer content can still be useful.

But the content value and the markup claim are two separate things.

Step 8: Make Your Brand Easy to Verify

Answer systems do not only process the text of one blog post.

Customers ask questions about companies:

  • What does this company do?
  • Who is it for?
  • Where does it operate?
  • How is it different?
  • Is it credible?
  • What evidence supports its claims?

Your website should answer those questions consistently.

Audit:

  • homepage positioning
  • About page
  • service names
  • author information
  • company details
  • location information
  • case studies
  • pricing statements where applicable
  • product/service descriptions
  • third-party profiles
  • review platforms
  • press coverage
  • social profiles

The goal is not to manufacture a fake consensus.

Google’s AI-search guidance specifically warns against seeking inauthentic mentions as a supposed generative-search shortcut. [2]

The goal is to reduce ambiguity with accurate, corroborated information.

Step 9: Build Real Third-Party Authority

Your own website is not the only place a customer learns about you.

Neither is it the only place an answer system may encounter information about your business.

Real third-party authority can come from:

  • legitimate press
  • industry publications
  • relevant directories
  • associations
  • review platforms
  • podcasts
  • YouTube
  • community discussions
  • partner websites
  • research citations
  • customer stories

The word real matters.

Do not astroturf Reddit.

Do not buy fake reviews.

Do not flood the web with low-quality guest posts that repeat the same claim.

Do not create dozens of shell websites to manufacture “independent” support.

A stronger strategy is to give people something worth referencing.

Original research is referenceable.

A useful calculator is referenceable.

A public dataset is referenceable.

A strong case study is referenceable.

A surprising experiment is referenceable.

An expert with a defensible point of view is referenceable.

That is brand authority with an underlying asset.

Step 10: Keep Information Fresh Without Faking Freshness

Freshness matters differently depending on the query.

A definition does not need to be rewritten every Tuesday.

A guide about a rapidly changing AI product might.

Microsoft recommends accurate sitemap lastmod values and explicitly warns against setting the modification time to the sitemap generation time when the page itself did not change. [11]

That is a good general principle:

Update content when the information changes, not because the calendar says you need a new date.

For AEO content, monitor:

  • platform documentation changes
  • crawler/user-agent changes
  • deprecated structured-data features
  • product renames
  • measurement-tool updates
  • new first-party reporting
  • incorrect brand information
  • source pages that disappear
  • questions gaining new search demand

A 2026 AEO guide with 2025 product names and retired Google features is worse than an older page that remains technically accurate.

Step 11: Measure AEO as a System, Not a Screenshot

AEO measurement should have multiple layers.

Layer 1: Eligibility

Track whether the page is:

  • crawlable
  • indexable where appropriate
  • accessible to the platform’s published search crawler
  • correctly canonicalized
  • returning a valid status code
  • internally discoverable

Layer 2: Search visibility

Track:

  • rankings
  • impressions
  • clicks
  • query coverage
  • landing-page visibility
  • generative AI impressions in Google Search Console

Layer 3: Answer visibility

Depending on the platform and available tooling, monitor:

  • citations
  • linked URLs
  • brand mentions
  • answer accuracy
  • competitor mentions
  • citation share across a controlled query set

Bing Webmaster Tools’ AI Performance report now provides first-party citation reporting for supported Microsoft AI experiences. [10]

Layer 4: Website response

Track:

  • ChatGPT referral sessions
  • other identifiable AI referrals
  • engagement
  • assisted conversions
  • form starts
  • booked calls
  • revenue

Layer 5: Business impact

Ask:

Did improved answer visibility create more qualified demand?

That is the metric that matters.

A brand mention is not automatically a lead.

A citation is not automatically revenue.

AEO should ultimately connect visibility to commercial outcomes.

A 30-Day AEO Operating Plan for 2026

AEO should be executed sequentially.

Days 0-3: Technical and measurement baseline

  • define the priority question set
  • record current brand visibility
  • verify Google Search Console
  • establish generative AI visibility baseline
  • review Bing Webmaster Tools AI Performance if available
  • verify OAI-SearchBot access
  • verify PerplexityBot access if Perplexity visibility matters
  • check robots.txt, WAF, CDN, indexing, canonicals, sitemap, and status codes
  • document existing referral traffic

Do not change content yet.

Measure first.

Days 4-7: Entity and information audit

  • review company and service descriptions
  • identify contradictions across the site
  • verify author and organization details
  • review high-value third-party profiles
  • list inaccurate AI answers that need correction
  • map every priority question to an existing or missing page

The output is an information-gap map.

Week 2: Improve existing pages before creating new ones

For pages that already target the right intent:

  • lead with a direct answer
  • remove outdated claims
  • add first-party evidence
  • improve internal links
  • clarify terminology
  • improve supporting media
  • verify structured data matches visible content
  • strengthen the commercial next step

Do not publish a new page when the correct page already exists and simply needs to become better.

Week 3: Create missing authority assets

Publish only where a genuine information gap exists.

Prioritize:

  • under-covered buyer questions
  • original research
  • comparison content
  • implementation guides
  • industry-specific resources
  • tools or calculators
  • case evidence

Connect every new page into the topic architecture.

Week 4: Re-measure and diagnose

Re-run the same query panel.

Review:

  • indexation
  • ranking changes
  • Google generative AI impressions
  • Bing citations
  • AI referrals
  • citation/mention shifts
  • answer accuracy
  • conversions

Then classify each asset:

  • scale
  • strengthen
  • restructure
  • consolidate
  • hold
  • stop

That is a real operating system.

Not “publish 20 AI SEO articles and hope.”

Common AEO Mistakes in 2026

Mistake 1: Treating AEO as a replacement for SEO

For Google, SEO fundamentals are still the foundation of its generative AI features. [1][2]

Fix: Build AEO on top of technical search eligibility and authority.

Mistake 2: Optimizing for every AI platform as if they were identical

Crawler controls, indexes, reporting, and retrieval systems differ.

Fix: Maintain a platform-specific control matrix.

Mistake 3: Believing special schema unlocks AI visibility

Google says there is no special schema required for AI Overviews or AI Mode. [1][2]

Fix: Use structured data only where supported and accurate.

Mistake 4: Publishing question pages for every wording variation

This creates thin content and cannibalization.

Fix: Consolidate queries by intent.

Mistake 5: Measuring only branded prompts

A brand is likely to appear more often when the question already contains its name.

Fix: Separate branded and unbranded query panels.

Mistake 6: Measuring only mentions

A mention can be inaccurate, negative, or commercially irrelevant.

Fix: Measure citations, accuracy, referrals, conversions, and revenue.

Mistake 7: Ignoring crawler and firewall access

A perfect article is useless to a search system that cannot retrieve it.

Fix: Audit bots, WAF rules, hosting, robots.txt, and indexability before rewriting content.

Mistake 8: Repeating third-party claims without verification

The AEO industry changes quickly, and unsupported “ranking factors” spread fast.

Fix: Label platform-confirmed facts separately from experiments and correlations.

What an AEO Agency Should Actually Do

If you hire an AEO agency in 2026, the engagement should be more rigorous than “add FAQs and schema.”

A serious scope should include:

  1. Search and answer baseline
    – query research
    – traditional rankings
    – current AI visibility
    – competitor citation analysis

  2. Technical discovery audit
    – Google indexation
    – crawler access
    – WAF/CDN checks
    – sitemaps
    – canonicals
    – platform-specific bot controls

  3. Information architecture
    – service pages
    – topic clusters
    – internal linking
    – question ownership
    – cannibalization control

  4. Content and evidence
    – answer clarity
    – first-party proof
    – original research
    – source quality
    – outdated claims

  5. Entity and brand consistency
    – company descriptions
    – authorship
    – third-party corroboration
    – review/profile accuracy

  6. Measurement
    – Search Console
    – Bing AI Performance
    – AI referral traffic
    – controlled prompt panels
    – leads and revenue

  7. Iteration
    – observe
    – diagnose
    – strengthen
    – remeasure

If an agency cannot explain how it will establish a baseline and prove whether the work changed anything, it does not yet have an AEO measurement system.

How WKND Approaches AEO

WKND treats AEO as part of a larger search-authority system.

The operating sequence is:

Business objective

↓

Question and keyword research

↓

Technical eligibility

↓

Entity and information architecture

↓

Answer-ready content

↓

Evidence and authority

↓

Platform-specific discoverability

↓

Measurement

↓

Revenue attribution

This matters because the objective is not to “win AEO.”

The objective is to generate useful visibility where customers are actually researching their decisions, then determine whether that visibility creates qualified demand.

That is also why WKND does not treat AEO, GEO, AI SEO, and traditional SEO as four disconnected departments.

They overlap.

The work should compound.

Learn about WKND’s integrated AI Advertising & SEO approach.

Is Answer Engine Optimization Worth It?

AEO is worth evaluating when your customers use answer-driven search during research, comparison, or purchase decisions.

The decision should be based on evidence.

Start by asking:

  • Do AI Overviews or AI Mode appear across important Google queries?
  • Are prospects using ChatGPT, Perplexity, Gemini, or Copilot to research your category?
  • Are competitors being cited where your company is absent?
  • Is your brand represented inaccurately?
  • Are measurable AI referrals or generative-search impressions already occurring?
  • Are high-value questions poorly answered by existing sources?

If the answer is yes, AEO becomes a visibility and information-quality problem worth addressing.

If the answer is no, do not manufacture an AEO project because the acronym is popular.

Fix the highest-value growth constraint first.

The 2026 AEO Principle to Remember

The goal is not to trick an AI into quoting you.

The goal is to become a source worth retrieving.

That requires:

  • access
  • clarity
  • evidence
  • authority
  • consistency
  • useful information
  • measurement

The best AEO work increasingly looks less like a collection of hacks and more like disciplined information engineering.

Make the right page discoverable.

Answer the right question.

Support the answer.

Connect it to the rest of the topic.

Make the company easy to verify.

Then measure what changed.

That is Answer Engine Optimization in 2026.

Frequently Asked Questions

What is Answer Engine Optimization?

Answer Engine Optimization is the practice of improving how a brand’s information is discovered, retrieved, represented, cited, and linked inside answer-driven search and AI experiences. It builds on SEO fundamentals but adds platform-specific discovery controls, question-focused content architecture, answer accuracy, citation visibility, and AI-search measurement.

Does AEO replace SEO?

No. For Google, AI Overviews and AI Mode are built on core Search systems, and pages still need to be indexed and eligible for Search to appear as supporting links. AEO extends SEO into answer-driven discovery and measurement rather than replacing it. [1][2]

How do businesses improve AEO in 2026?

Start with a repeatable visibility baseline, verify technical and crawler access, map customer questions to existing pages, improve answer clarity and evidence, strengthen entity consistency and real third-party authority, then measure citations, generative AI impressions, referrals, and conversions.

Does schema markup help with AEO?

Structured data can support normal search understanding and rich-result eligibility when it accurately matches visible page content. However, Google says no special schema is required for AI Overviews or AI Mode and warns publishers not to overfocus on structured data as a generative-AI optimization tactic. [1][2]

Should I use FAQ schema for AEO?

Do not use FAQ schema as a shortcut for Google AI visibility. Google deprecated FAQ rich results in May 2026 and removed the FAQ rich-result documentation in June 2026. FAQ content itself can still be useful to readers, but that is different from claiming FAQ markup is an AI-ranking mechanism. [4]

How do I make my site discoverable in ChatGPT Search?

OpenAI says publishers should make sure they are not blocking OAI-SearchBot if they want their public content to be discovered, surfaced, cited, and linked in ChatGPT Search. OpenAI does not guarantee placement. [8]

How do I measure AEO performance?

Use several layers: technical eligibility, normal search visibility, generative AI impressions, citations or linked URLs where first-party reporting exists, AI referral traffic, answer accuracy, leads, assisted conversions, and revenue. Google and Microsoft both introduced dedicated 2026 reporting for visibility in their generative AI search experiences. [5][10]

Sources

  1. Google Search Central, “AI features and your website”
    https://developers.google.com/search/docs/appearance/ai-features

  2. Google Search Central, “Optimizing your website for generative AI features on Google Search”
    https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

  3. Google Search Central, “Google Search technical requirements”
    https://developers.google.com/search/docs/essentials/technical

  4. Google Search Central, Search documentation updates, May-June 2026 FAQ rich-result deprecation/removal
    https://developers.google.com/search/updates

  5. Google Search Central, “Introducing Search Generative AI performance reports in Search Console”
    https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports

  6. Google Search Central, “Google Search Essentials”
    https://developers.google.com/search/docs/essentials

  7. Google Search Central, “Guidance on generative AI content”
    https://developers.google.com/search/docs/fundamentals/using-gen-ai-content

  8. OpenAI, “Publishers and Developers – FAQ”
    https://help.openai.com/en/articles/12627856

  9. Perplexity, “Perplexity Crawlers”
    https://docs.perplexity.ai/docs/resources/perplexity-crawlers

  10. Microsoft Bing Webmaster Blog, “Introducing AI Performance in Bing Webmaster Tools Public Preview”
    https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview

  11. Microsoft Bing Webmaster Blog, “Keeping Content Discoverable with Sitemaps in AI Powered Search”
    https://blogs.bing.com/webmaster/July-2025/Keeping-Content-Discoverable-with-Sitemaps-in-AI-Powered-Search

  12. Microsoft Bing Webmaster Blog, “Bing Introduces Support for the data-nosnippet HTML Attribute”
    https://blogs.bing.com/webmaster/October-2025/Bing-Introduces-Support-for-the-data-nosnippet-HTML-Attribute