Tag: AI optimization

Harnessing AI Patterns for Superior Content Creation

The past year has been a whirlwind as we all tried to grasp how to report on AI visibility and understand what it truly takes to be seen and cited by AI models.

Rand Fishkin’s recent study on the variability of AI responses pointed out how LLM outputs differ significantly from the stable and predictable nature of search rankings, making this KPI a challenging aspect of the analytics landscape.

The research illustrates a less than 1% chance that ChatGPT or Google AI will provide the same brand list in two different responses. They scrutinized thousands of prompts across various LLMs, revealing their unpredictable nature.

This unpredictability has led some in the SEO community to question the value of rank tracking on a broad scale. Despite these challenges, rank tracking remains a valuable, albeit misapplied, tool.

While AI response tracking is currently an unstable KPI, it proves to be incredibly potent when used as an analytical tool to inform content strategy.

I’m diving into why we should continue investing in prompt tracking and how this effort can illuminate our content strategy.

Why AI Visibility Tracking is Currently Unreliable

Understanding that language learning models aren’t deterministic ranking machines is crucial. They are probabilistic, synthesizing information from trained data or live searches, providing varying answers influenced by context and intent.

Responses shift depending on the prompts, and identical questions can be phrased in multiple ways, which can lead to challenging questions from your CMO about why certain prompts do not feature your brand despite previous citations. It’s a natural outcome in the evolving landscape of AI-driven visibility.

Even though tracking visibility might be uncertain until user prompting becomes clearer, it remains a valuable aspect of SEO analytics.

If we consider prompt response tracking not as a stable KPI but as a pattern analysis, it becomes something SEOs are already quite familiar with.

Shifting focus from merely checking if you are cited or listed to understanding how responses are structured offers more insightful strategies. Analyze these factors:

The structure of the response.
Recurring concepts.
Key phrases and terms.
Typical levels of detail involved.

This shift in mindset is imperative.

Traditional SEO vs. AI Pattern Analysis

Traditional SEO involves reverse engineering rankings, whereas AI search encourages us to apply this method by uncovering patterns in AI-generated results.

Traditional SEO	AI Pattern Analysis
Focus on rankings	Understanding concept synthesis
Content gap analysis	Topic associations
Fixed SERP results	Dynamic AI responses
Determined signals	Probability-driven responses

Through analyzing prompt response patterns, we can dive deep into content-level concept synthesis, beyond the technical framework.

In defining a pattern, look for the themes and recurring topics rather than exact response consistency across outputs.

Each LLM formats its outputs uniquely, yet patterns often emerge within the structures, despite differing retrieval methods and functionalities.

For identifying a pattern:

It appears in 75% or more outputs.
Observed across two different AI models, like GPT and Gemini.
Present across multiple prompts in a consistent way.

The 75% benchmark felt stable enough for my sample sizes to confirm strong patterns rather than randomness. You can adjust this based on your content and context, but this approach has helped me sift consistency from the noise.

For instance, if “pricing transparency” shows up in 9 out of 12 responses and across two models, that indicates semantic relevance—a crucial insight into your content strategy.

The Framework to Implement

Here’s how you can apply this for yourself with a structured framework.

Segment your analysis into the following pattern types:

Structural patterns.
Conceptual patterns.
Entity patterns.

Structural Patterns

Focus here on the organization of responses, identifying aspects like:

Header and section frequency.
Consistency in list formatting.
Order or procedural steps.
Framing of pros/cons.
Comparative tables.
Decision-making frameworks.

These indicators can show how models structure topics.

For example, if your prompt’s outputs repeatedly follow: Definition > Criteria > Tools > Implementation, that’s a structural pattern. Use it to gauge user preferences, although it’s crucial to remember that AI suggestions are just tools to enhance content alignment.

Conceptual Patterns

These vary per topic. They might require deeper analysis to uncover. For example, when focusing on “Best domain registrars,” you might look for:

Pricing transparency (renewal and purchase).
Customer service references.
Inclusion of addons (e.g., WHOIS privacy, free emails).
Security features.
Bundling opportunities.
Transfer processes.

If renewal pricing often emerges in different models and variations, adjust how you frame and discuss it in your content pieces to reflect high relevance.

These patterns offer insight into decision-making associations within AI model frameworks.

Entity Patterns

Examine the appearance of brands, tools, and references in responses, noting:

Mentions of specific brands.
Tool or feature associations with brands.
Category positioning within context.
Sourced citations and their relevance.

Evaluate how certain features align with specific brands, or notice frequently cited sources. This evaluation helps in assessing brand positioning and opportunities, maybe even within affiliate environments or third-party collaborations.

Constructing Your System

It’s not necessary to invest heavily in prompt-tracking tools, although they simplify the process—I manage with manual tracking, which, despite not being perfect, serves its purpose effectively.

If you’re working solo, adjust the methodology to fit your capacities. This might involve extended tracking periods or lowering pattern consistency thresholds from, say, 75% to a more feasible 60%.

Step 1: Choose and Cluster Your Prompts

Identify three main topics to monitor. Develop 3–5 variations of prompts for each topic.

For example, if one topic is domain registration, my cluster includes:

How do I register a domain name?
How can I get a domain name?
Where can I buy a domain?

Step 2: Create Your Tracking Sheet

To track responses, consider using a simple spreadsheet with columns like this:

Prompt

LLM

Web Search? (Y/N)

Date

Response

Sources (if applicable)

Is My Brand Mentioned?

Track LLM versions under the appropriate column to understand when new versions are released and how they impact your data.

Begin capturing this data, then enhance the sheet as needed to include pattern elements. Tools like Claude or ChatGPT can assist in automation, reducing manual labor.

Step 3: Develop a Tracking Plan and Begin Monitoring

To ensure effectiveness, define:

Which AI models to track.
Options for search mode—enabled, disabled, or model-decided.
The prompt frequency to run each test on each model.
Tracking schedule or frequency.

Engage team members wherever possible and use private modes to reduce contextual biases.

Every week, my team tests each prompt on platforms like ChatGPT and Perplexity, collecting several responses per prompt per model consistently.

Step 4: Conduct Analysis

Once you compile 20-30 responses per prompt, delve into the analysis phase. Select tools to streamline this process effectively.

Identify recurring patterns and link these insights to your site’s relevant pages. Ensure your content addresses discovered themes and questions, and consistently represents the patterns found.

Assess and revise consistently, making this analysis an integral part of your optimization strategy.

Beware of AI Pattern Analysis Pitfalls

AI is inherently probabilistic and not always correct. While it shouldn’t be the sole basis of your strategy, it can offer valuable insights to enhance your playbook.

Risks such as bias in training data, uncertainty in whether search or training data was utilized, and differences in new model launches across LLMs persist.

Use judgment and audience insights to determine when AI responses align with your optimization goals.

Linking Your Strategy to Performance

This is where it gets complex. Though AI responses are notoriously unpredictable, some measurable signals can reflect your content’s impact.

“Traditional” Metrics: Are you seeing better click rates or improved positions in tools like GSC? Are conversions increasing?
AI Traffic Monitoring: Analyze AI traffic data from platforms like Adobe or GA4 to note changes on updated pages.
AI Tracking Tools: While there’s variability here, if utilizing AI visibility tools, they might indicate the effectiveness of your strategy and reflect brand patterns using manual tracking as well.

I recommend experimenting with this manual tracking approach to witness potential brand emergence as a pattern and gain brand visibility.

Begin Examining AI Outputs

Indeed, many unknowns surround LLMs, seemingly changing daily. Yet, one constant remains: these tools provide insights. Leverage any understanding of these responses to enhance your strategies.

Patterns in responses can unravel how subjects are interpreted, how brands appear, and offer guidance on adapting your content strategy.

Inspired by this post on Search Engine Land.

February 26, 2026

Mastering AAO: The Future of SEO is Here
Incomplete terminology often results in an incomplete strategy. To bridge this gap, I’m here to offer a clearer framework for optimizing when AI systems both recommend and act.

Search engine optimization (SEO) – be found. Answer engine optimization (AEO) – be the answer. AI engine optimization (AIEO) – be the recommendation. Lastly, assistive agent optimization (AAO) – be chosen when there’s no human in the loop. These are four distinct stages, each absorbing the one before it.

The constant term across the latter two stages is “assistive.” It highlights the purpose: what the system provides the user. The shift happens when “engine” becomes “agent,” marking our industry’s move from systems that recommend to those that act.

For me, this naming debate distracts us from the real work. The SEO industry has splintered across multiple terms that essentially describe the same discipline. Each term has its advocates, and while debating these labels, we aren’t progressing with the actual work.

So, let’s cut to the chase: I’ll lay out why AAO is an effective solution so we can all get back to focusing on our jobs.

Every competing acronym offers partial coverage, none captures it all

Every AI system making recommendations or autonomous decisions—be it Google, Bing, ChatGPT, Perplexity, or Copilot—relies on three components: large language models, knowledge graphs, and traditional search. I refer to these as the algorithmic trinity.

The balance of these elements differs by platform, but the trinity itself remains universal. Even those at Google I’ve conversed with agree on this architectural structure.

SEO has always described the engine’s purpose, which I’ve appreciated. Let’s examine how the competing acronyms align against these three components.
- GEO describes the mechanism over intent. It involves the LLM layer, includes search as necessary, but overlooks the knowledge graph entirely. This technology-specific term lacks longevity when the technology advances.
- Entity SEO covers the knowledge graph layer but only acknowledges search as a delivery mechanism and LLMs secondarily. It fails the glossary test, often confusing non-specialists.
- LLM optimization candidly reveals its scope but neglects the knowledge graph and search components entirely.
- AI SEO tacks the term “AI” onto the traditional term, making it accessible to outsiders but lacking durability. As we move to 2026, users are more likely researching rather than searching.
All these terms are incomplete, and it naturally follows that incomplete terminology leads to incomplete strategy. Practitioners tend to optimize only for the part their acronym emphasizes, neglecting others.

Assistive agent optimization (AAO) evolves cleanly from answer engine optimization and encompasses everything required for crafting a comprehensive strategy:
- “Assistive” clearly defines the purpose for the entire algorithmic trinity.
- “Agent” identifies the actor deploying all three components to reach a decision.
- “Optimization” captures what we do.
It’s a stable three-legged stool, ensuring consistency, much like sitting on a stool with evenly matched legs—one that doesn’t wobble.

Explore further: SEO, GEO, or ASO? What to call the new era of brand visibility in AI [Research]

The glossary test shows AAO isn’t flawless, but it’s our best option

Generative engine optimization, entity SEO, and LLM optimization all require niche understanding, failing the glossary test.

Although “assistive” in AAO isn’t instantly recognizable, “agent” is now a part of popular vocabulary. We see every tech company promoting agents, and “optimization” is self-explanatory. Two out of three terms land smoothly, and the third is easily understood.

If you can propose a more fitting term that perfectly covers the algorithmic trinity and passes the glossary test, I’m open to it. After all, what matters is the discipline, not the terminology.

Importantly, AAO describes a role: optimizing so the assistive agent favors your brand. Roles endure beyond technologies. The right term will endure for years, independent of prevailing model architectures or retrieval methods.

What changes when you adopt the AAO framework

Your brand identity becomes foundational rather than optional. When an agent reviews hotel options, supplier choices, or consultant recommendations, it doesn’t thumb through pages seeking the best title tag. Instead, it assesses the brand: its essence, service, audience, reliability, and confidence in those facts.

This trust originates from the entity home—the page you own that roots everything the algorithmic trinity knows about your brand—and extends through all corroborating sources. If your brand isn’t clearly understood, the agent will select one that is.

The funnel resides within the agent now. The well-trodden acquisition funnel (awareness, consideration, decision) used to bounce users around, with search engines acting as traffic sources. Now, under AAO, this entire journey takes place within AI, without users encountering a list of options. The agent becomes aware of, evaluates, and decides on your brand before presenting the result. Your mission is thus to ensure your brand is the answer when the agent processes its funnel internally.

You might think, “We’re not there yet.” Yes, that’s true for most, but the funnel is already within the assistive engine. With platforms like ChatGPT, Perplexity, Google AI Mode driving users to the perfect click—the pinnacle in AI zeroing in on a single user solution—most tend to accept what’s presented. What’s presently lacking is the agent making the purchase decision.

The web index is no longer the sole source of truth it once was. For two decades, it dominated, but that monopoly is crumbling:
- Proprietary datasets feed agents directly, evolving search into what I term ambient research, where in-app pushes surface brand suggestions without a query.
- Agents and engines utilize APIs, booking systems, and internal databases that don’t intersect traditional web indices. The index will persist as an essential anchor, but it’s no longer the sole gatekeeper. It’s time we strategize with that understanding.
The push layer is also resurfacing. For years, we depended on search engines to understand our content—rendering JavaScript, deciphering complex pages—and they responded. This passive approach will continue, but proactive methods are gaining ground.

IndexNow, nurtured by Fabrice Canel at Bing, along with MCP and whatever Google deploys next, all facilitate one key function: enabling us to push structured data to action-oriented systems instead of waiting for them to retrieve it. It’s reminiscent of the 1990s, with proactive URL submissions and active ecosystem feeding.

Google’s absence from IndexNow isn’t due to the concept’s flaws—it’s quite ingenious—but perhaps because it wasn’t Google’s brainchild, sparking aspirations for a proprietary adaptation.

We must also consider that JavaScript rendering was Google’s generous favor, not an industry standard. Many AI agent bots don’t process JavaScript, so content reliant on client-side rendering may never be seen by an increasing number of agents.

(This all aligns with the 10-gate DSCRI-ARGDW pipeline, which I’ll detail in the next series segment.)

Further reading: The origins of SEO and what they mean for GEO and AIO

Your SEO skills remain relevant; the focus shifts from engines to agents.

You don’t need to perfect each intermediary step before embracing AAO, as AAO encompasses AIEO, AIEO encompasses AEO, and AEO encompasses SEO—the skills stack remains, only the focus shifts: aim to be chosen by the agent, recommended during research, and mentioned during inquiries.

The compounding advantage discussed in “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it” applies here as well. Our top performers secured 59.5% of all citability by February, rising from 30.9% in December—a notable 293% increase in concentration over two months.

Those adopting this perspective will consistently build pipeline confidence while others remain entangled in debates over acronyms, further widening the gap over time.

The discipline now has a name, the agents are already operational, the push layer is in play, and the era of complacency has ended.

The initial two articles explored the “what” and the “why.” Next week, I’ll delve into the “how.” I plan to unveil the 10-gate pipeline I’ve been referring to: DSCRI-ARGDW, a crucial conduit between your content and a conversion by an AI engine.
- Discovered: The bot becomes aware of your existence.
- Selected: The bot deems your data worthy of retrieval.
- Crawled: The bot captures your content.
- Rendered: The bot transcribes what it retrieves into a readable form.
- Indexed: Content is committed to the algorithm’s system memory.
- Annotated: The content undergoes classification across various dimensions.
- Recruited: The algorithm leverages your content.
- Grounded: The content’s credibility is confirmed against multiple sources.
- Displayed: The content is showcased to the user.
- Won: The moment of triumph – the engine secures the perfect click.
Inspired by this post on Search Engine Land.
February 24, 2026
Master AI Visibility: Your 2026 Guide to Generative Optimization
Ever wondered how to get your brand noticed by AI search engines? Let me walk you through the step-by-step process of getting your brand cited, recommended, and discovered by AI search platforms.

So, let me dive into the world of AI! Gartner forecasts a 25% drop in traditional search volume as AI engines take precedence. With Google’s AI Overviews attracting over 2 billion users monthly, and ChatGPT serving 800 million users weekly, the shift is here.

Gone are the days of just vying for a spot on Page 1. Now, it’s all about becoming the go-to source that AI engines cite in their answers.

This focus on generative engine optimization (GEO) is crucial in 2026. Here’s how to craft a GEO strategy that truly delivers.

What is GEO — and why 2026 is the tipping point

GEO is about aligning your content and digital identity so AI search platforms like ChatGPT, Google AI Overviews, Perplexity, and others, can easily find and recommend your brand.

If traditional SEO got you among the top 10 links, GEO aims to secure your position among the few domains cited in AI responses. It’s tougher in terms of competition, but the credibility from being mentioned by an AI engine is worth it.

Several forces make 2026 a milestone year. Users are becoming loyal to specific AI platforms, elevating GEO from experimental to essential. Universities and enterprises are backing this shift, highlighting AI engines’ preference for authoritative external sources over internal content.

Understanding this trend is vital for building an effective GEO strategy.

A practical GEO framework: assess, optimize, measure, iterate

Treating GEO as a mere content tweak is a misconception. Just like SEO, it requires ongoing commitment. Here’s a repeatable framework to master it.

Phase 1: Assess your AI search readiness

You need a baseline before optimization. Many brands monitor Google rankings but are blind to how AI engines portray them.

Ask yourself crucial questions: Are AI engines referencing your content? Can they read your structured data efficiently? How does your brand appear in AI-generated content? Are your competitors cited where you aren’t?

Consider using tools like Geoptie’s free GEO Audit for a quick assessment, providing actionable insights for optimization.

Phase 2: Optimize your content for AI engines

The heart of your GEO strategy is optimization. Focus on content structure, entity authority, technical foundations, and keeping content up-to-date.

Structure content for AI retrieval

AI breaks down content to assess relevance and clarity. Make sure each section stands independently.

Begin sections with straightforward answers followed by context. Use headings properly and add TL;DR summaries to enhance retrieval chances. FAQs are crucial as AI relies heavily on Q&A formats.

Build entity authority

GEO emphasizes brands and entities rather than single pages. Strengthen these signals for better recognition and citation by AI engines.

Ensure brand mentions are consistent, develop comprehensive about and author pages, and maintain a Wikipedia presence if applicable. A well-managed knowledge panel is also beneficial.

AI engines prefer coverage from third parties over personal content. Thus, digital PR and thought leadership have become essential GEO components.

Nail the technical foundations

Technical optimization in GEO includes traditional SEO elements plus AI-specific enhancements.

Utilize schema markup, verify robots.txt settings accommodate AI crawlers, and consider adding an llms.txt file to guide AI interactions with your site.

Don’t forget the basics. Fast load times, clean architecture, and mobile optimization remain crucial.

Prioritize freshness and depth

AI values recency in sources. A guide from 2024 without updates will be overshadowed by a 2026 version on the same subject.

Keep cornerstone content refreshed with up-to-date data and insights, distinctly marked with a “Last updated” timestamp. Original research and exclusive data enhance your chances of being cited by providing unique value.

Phase 3: Measure your AI search performance

Measurement is often a missing piece in GEO strategies. Many marketers lack clear insights into AI search visibility after mastering traditional SEO metrics.

Important metrics include AI citation frequency, share of voice, citation sentiment, and AI-referred traffic. Traditional tools fall short in tracking these, necessitating specialized GEO platforms.

Geoptie’s free Rank Tracker is a convenient way to check your standing on various AI platforms as an initial assessment.

Phase 4: Iterate and scale

GEO doesn’t end after initial implementation. The AI landscape continuously evolves, requiring rapid adaptation.

Analyze performance data to understand citation success and refine strategies. Focus on platforms delivering the most value and monitor competitor movements.

Replicate successful content across various formats and integrate GEO tasks among content, SEO, PR, and product teams.

Geoptie offers a comprehensive dashboard for managing audits, competitor analysis, citation tracking, and content optimization all in one place, simplifying the GEO workflow.

Now is the time to build GEO capability

GEO is not a fleeting trend. As AI adoption surges in 2026 and beyond, an early commitment to GEO sets the stage for long-term success.

Follow this clear playbook:
- Assess your current standing
- Enhance your content and technical readiness for AI
- Track performance on relevant platforms
- Iterate continuously
Brands laying this foundation will reap ongoing benefits as AI becomes a primary tool for customer engagement.

The crucial decision is whether you’ll pioneer or be a follower in GEO.

Ready to take control of your AI visibility?

With Geoptie, you have a one-stop solution for mastering GEO. From in-depth audits to tracking AI rankings, competitor analysis, and crafting AI-specific content, Geoptie equips you from the start.

Whether beginning your GEO journey or scaling an existing plan, Geoptie helps translate insights into real progress. Start your free 14-day trial to gauge your brand’s AI search standing.

Inspired by this post on Search Engine Land.
February 23, 2026
Unveiling Google’s AI Search: Classic Methods Meet Modern AI

As someone deeply fascinated by how AI influences search engines, it’s intriguing to know that behind Google’s AI search facade, there is a robust system at work. This system diligently narrows down tens of thousands of documents to just a handful, relying heavily on traditional signals for visibility.

Jeff Dean, Google’s chief AI scientist, recently shared some insights on the Latent Space: The AI Engineer Podcast, where I learned how much Google’s AI still draws from its classic search engine architecture.

The architecture: filter first, reason last. In essence, for any content to be visible, it must navigate through various ranking thresholds. It starts with entering a broad candidate pool, goes through intense reranking, and only then becomes part of an AI-generated response. Essentially, AI builds on top of traditional ranking metrics.

Dean elaborated that an LLM-powered system doesn’t skim through the entire web in a single go. Instead, it begins with Google’s comprehensive index, utilizing lightweight techniques to sift through a large pool of potential documents. Dean described this process:

“You start by pinpointing a subset that seems relevant using very lightweight methods. Initially, you might have around 30,000 documents, and this number gradually refines as increasingly sophisticated algorithms and signals are applied, ultimately leading to the final 10 results or so.”

These robust ranking systems further trim this set. Consequently, it’s only after multiple filtering rounds that the most capable model steps in to analyze a significantly smaller group and generates a response. Dean continued:

“An LLM-based system isn’t vastly different. Although it processes trillions of tokens, it seeks the key 30,000-ish documents with those maybe 30 million significant tokens. From there, it derives the crucial 117 documents needed to accomplish the task.”

Dean referred to this as an “illusion” of engaging with trillions of tokens. In practice, it’s a structured pipeline: retrieve, rerank, synthesize. Dean elaborated:

“Google search isn’t about an illusion; it’s genuinely searching the internet but distilling it down to a very relevant subset.”

Matching: from keywords to meaning. Although it’s not novel, emphasizing that comprehensive topic coverage is more important than repeating exact keywords was refreshing.

Dean explicated how LLM-based representations revolutionized query-to-content matching by moving beyond word-for-word alignment. Now, Google evaluates whether pages or even paragraphs are topically relevant to a given query. He explained:

“Implementing an LLM-based text representation means we’re no longer bound by the need for specific words on a page. Instead, we delve into the topical relevance of a page or paragraph to a query.”

This paradigm shift allows Search to connect queries to answers notwithstanding different phrasings, increasingly focusing on intent and subject matter rather than mere keyword placements.

Query expansion didn’t start with AI. Dean highlighted Google’s 2001 achievement of moving its index into memory, enabling swift query expansion. He noted:

“We significantly scaled in 2001, wanting a larger index for better retrieval, accommodating growing traffic through a sharded system, evolving to fit the entire index in memory across machines. This dramatically improved query quality.”

Before this, expanding queries with additional terms was cost-intensive due to disk accesses. Once the index resided in memory, Google could enrich short queries with synonyms and variations to capture broader meanings. Dean recalled:

“Previously, term lookup was constrained by disk seek penalties. Post-memory transition, handling 50-term queries became feasible, enhancing definition and meaning extraction, far ahead of LLMs.”

This transition steered Search towards intent and semantic matching, setting the stage for today’s LLM-driven advancements, which amplify meaning-based retrieval through more refined systems and advanced computing power.

Freshness as a core advantage. Dean’s insights revealed that one of Search’s pivotal transformations involved accelerating update rates. Early on, pages refreshed monthly. Now, Google’s systems can refresh in under a minute. He observed:

“Google’s early index expansion coincided with ramping up refresh rates, now a vital parameter. Swift updates remain crucial.”

This advancement significantly enhanced news search results and overall user experience, as current data is a consumer expectation. Dean added:

“A stale index, like last month’s news, loses utility fast.”

Google’s sophisticated systems decide the frequency of page crawls, weighing potential change against the value of the latest version. Even less frequently updated important pages might be crawled often due to high update value. Dean shared:

“An intricate system determines update rates and page importance, ensuring often-updated important pages remain current.”

Why I find this crucial. The fascinating aspect is realizing that AI answers don’t bypass fundamental elements like ranking, crawl prioritization, or relevance signals. These aspects remain critical. Although LLMs reshape content synthesis and presentation, they don’t circumvent the underlying search mechanics essential for eligibility and quality.

Listen to the full interview. Discover more insights from Owning the AI Pareto Frontier — Jeff Dean.

Inspired by this post on Search Engine Land.

February 17, 2026
Cloudflare’s Markdown Feature: A Game Changer or a Cloaking Risk?

Yesterday, I stumbled upon some exciting news from Cloudflare. They’ve introduced a feature called Markdown for Agents, which provides machine-friendly versions of web content alongside the traditional pages we all see.

Cloudflare describes this update as a proactive measure in response to increasing AI crawler activities and agentic browsing.

When a client requests text/markdown, Cloudflare fetches the HTML from the origin server, converts it right at the edge, and then hands over a Markdown version.

Interestingly, the response includes a token estimate header, which helps developers like me manage context windows more effectively.

Early feedback highlighted not only the efficiency gains but also the potential implications of offering alternate representations of web content.

What’s happening. Being part of the 20% of the web that Cloudflare powers, I learned that Markdown for Agents utilizes standard HTTP content negotiation. If a client sends an Accept: text/markdown header, Cloudflare immediately converts the HTML response on-the-fly to Markdown format. The response, marked with Vary: accept, ensures caches store separate versions.

Cloudflare views this opt-in feature as a shift in content discovery and consumption, benefitting AI crawlers and agents with its structured text that requires less overhead.

They claim Markdown can reduce token usage by up to 80% compared to HTML, which is quite impressive!

Security concern. SEO consultant David McSweeney raised a concern, citing that Cloudflare’s Markdown for Agents feature might make AI cloaking incredibly simple because the Accept: text/markdown header tips off origin servers that the request is AI-related.

Regular requests deliver the usual content, but those for Markdown can trigger a unique HTML response that gets converted for AI consumption, McSweeney explained on LinkedIn.

The worry is that sites might inject hidden instructions, altered product data, or other machine-only content, creating a hidden “shadow web” for bots, unless the header is stripped before reaching the origin.

Google and Bing’s markdown smackdown. Here’s the kicker. Representatives from Google and Microsoft advised against creating separate markdown pages for large language models. Google’s John Mueller noted:

“Given that LLMs have always trained on and parsed normal web pages, it seems obvious they have no issues with HTML. Why serve a page that no end user sees? Plus, if they validate equivalence, why not stick to HTML?”

Microsoft’s Fabrice Canel added:

“Do you really want to double crawl load? We’ll check for similarity anyway. Non-user versions (like crawlable AJAX) are often neglected and broken. Human oversight fixes both user and bot views. Schemas help, and AI makes us even better at deciphering web pages. Less is more in SEO!”

Cloudflare’s feature doesn’t generate another URL but does create varied representations based on request headers.

The case against markdown. Technical SEO consultant Jono Alderson pointed out that once a machine-targeted representation exists, platforms must choose to trust it, verify it against the human version, or outright ignore it:

“Flattening a page to markdown doesn’t only remove clutter. It strips away judgment and context.”

“The instant you publish a machine-exclusive page representation, you craft a secondary candidate version of reality. Regardless of source promises or claims of identical content, a system now views two representations and must determine the true reflection of the page.”

Dig deeper. Why LLM-only pages aren’t the answer to AI search

Why we care. With Cloudflare’s advancements, AI ingestion might become more cost-effective and streamlined. But does serving distinct content to humans and crawlers verge on cloaking? Stay tuned…

Inspired by this post on Search Engine Land.

February 13, 2026
Master GEO: Elevate Your Brand’s Visibility in AI Responses

Welcome to my comprehensive guide on Generative Engine Optimization (GEO). In this ever-evolving digital landscape, mastering GEO has become essential for anyone wanting to enhance their brand’s visibility in AI-driven responses on platforms like ChatGPT, Gemini, Perplexity, and Claude.

I’ve compiled the latest strategies and data to help you navigate this dynamic area. By following these insights, you’ll not only improve how your brand appears but also engage more effectively with AI-optimized content, ensuring you stay ahead in the competitive digital marketing arena.

Join me on this journey to master GEO and transform your approach to online branding and content visibility. With focused strategies, my guide covers everything you need to know to make informed decisions and attain greater engagement with your audience.

Inspired by this post on genmark.ai Blog.

February 12, 2026
Boosting B2B Visibility: How to Shine in AI-Driven Vendor Searches

B2B buyers are increasingly turning to ChatGPT when conducting vendor research, and I’ve discovered how essential it is for B2B companies like mine to stand out in this AI-powered landscape.

As I navigate this digital revolution, I focus on building visibility in AI search to ensure my business is consistently recommended during the buying process. Here’s how I’ve approached it and why it’s vital.

In today’s world, mastering the art of AI Search Optimization is not just beneficial; it’s necessary. By integrating key strategies into my B2B marketing plan, I’ve learned how to effectively leverage AI tools to stay ahead in the competitive marketplace.

Inspired by this post on genmark.ai Blog.

February 12, 2026
Discover How Ads Enhance Your ChatGPT Experience
On the OpenAI podcast, I recently listened to Andrew Maine as he spoke with OpenAI executive Assad Awan. During their conversation, Awan shared insights into how ads are being introduced to ChatGPT, who will see them, and the measures in place to protect user trust.
Who will see ads:
Ads will be visible to users on the Free and Go tiers. As for Plus, Pro, and Enterprise subscribers, they will not encounter ads in their interactions. Additionally, Enterprise workspaces are staying completely free from advertisements.
The guardrails: Awan highlighted that OpenAI is committed to structuring ads with strict trust principles in mind.
- Separation: Ads are distinctly separate both visually and technically from the model answers.
- Privacy: Conversations are not shared with advertisers, ensuring privacy is upheld.
- Sensitive topics: Discussions on health, politics, and other sensitive subjects will never be interrupted by ads.
- Controls: Users have the ability to adjust ad personalization settings or even upgrade to remove ads entirely.
Awan also mentioned that the AI model itself is not aware of when ads are present and will only reference them if directly queried by a user.
Zoom in. OpenAI emphasizes prioritizing user trust over other factors such as user value, advertiser value, and revenue. This framework is designed to prevent ads from influencing the model’s responses.
For small businesses. Awan envisions a future where AI simplifies advertising for small businesses. By understanding plain language goals, AI can help run campaigns without the complexity of traditional dashboards.
Why we care. ChatGPT ads promise a unique, high-intent channel where businesses can connect with users during their active conversations and decision-making processes. By focusing on relevance and AI-driven matching, the platform can lower the entry barrier for small to midsize advertisers while boosting performance for larger brands.
Should OpenAI succeed in cultivating a trusted ad environment, it could reshape how advertisers perceive discovery and customer engagement within AI-driven platforms.
What’s next. The initial ad tests will remain conservative, concentrating on utility and relevance before volume as OpenAI hones ad formats and placements.
The big picture. Through advertising, OpenAI aims to expand ChatGPT access while adhering to a trust-first design—a balance they assert is key to their long-term strategy.
Dig deeper. Watch the full interview with Assad Awan

Inspired by this post on Search Engine Land.
February 11, 2026
Microsoft Launches Game-Changing AI Content Marketplace

I’m thrilled to share that Microsoft Advertising has just unveiled the Publisher Content Marketplace (PCM). This innovative system allows publishers like us to license premium content to AI products and earn revenue based on its usage.

How It Works. At its core, PCM creates a direct value exchange. As a publisher, I have the freedom to set my own licensing and usage terms. Meanwhile, AI developers can discover and license this content for grounding their algorithms in real-world scenarios. The marketplace also offers detailed usage reports, providing insights into how our content performs and where it contributes the most value.

Designed to Scale. The PCM is a scalable solution designed to eliminate the need for one-off licensing deals. Participation is entirely voluntary, and ownership and editorial independence remain with the publishers. It’s a platform inclusive of everyone from large global publishers to smaller niche outlets like ours.

Why We Care. As AI technology progresses from merely answering questions to making impactful decisions, the quality of content is becoming increasingly crucial. Whether it’s about influencing purchases, finance, or healthcare, AI systems need to tap into premium content, elevating the importance of credibility and trust in our brands.

Early Traction. Microsoft Advertising has partnered with notable U.S. publishers such as Business Insider, Condé Nast, and Hearst to co-design PCM. Initial pilot projects anchored Microsoft Copilot responses to licensed content, with companies like Yahoo as early adopters.

What’s Next. Looking ahead, Microsoft plans to extend the pilot program to more publishers and AI developers who share the belief that as the AI web evolves, the value and governance of high-quality content should be recognized and rewarded.

The Big Picture. In the evolving landscape of AI-driven web interactions, tools are now summarizing, reasoning, and making recommendations through conversation. The effectiveness of these tools hinges on access to trusted and authoritative sources, many of which are under paywalls or in secured archives.

The Tension. The traditional model where publishers provide content in exchange for traffic from platforms is changing. AI is increasingly delivering answers directly, which reduces clicks but still relies on high-quality content.

Bottom Line. For AI to make better decisions, it must have access to superior inputs. Microsoft’s PCM is a strategic move towards establishing a sustainable content economy that supports the next wave of AI innovation.

Microsoft’s Announcement. Learn more about this initiative in Microsoft’s blog post on Building Toward a Sustainable Content Economy for the Agentic Web.

Inspired by this post on Search Engine Land.

February 3, 2026
Master AI Search: Boost Visibility with 12 Proven Tactics

One of the biggest challenges I face in SEO isn’t AI itself—it’s battling the wave of misinformation about it.

SEO isn’t dying — it’s evolving. So, I need to be proactive in understanding these changes and be discerning about the voices I trust in the industry.

I’m not easily surprised, but some of the AEO (or GEO) talks I attended last year were genuinely shocking—even for someone like me who may have had a bit of Botox.

I recall one speaker apologetically addressing a room of marketers, only to promptly suggest outdated tactics as the “secret sauce” for LLM visibility. It was painful to witness.

Thankfully, trusted voices like Lily Ray, Kevin Indig, Steve Toth, and Ross Hudgens came together this week for an enlightening roundtable on the future of search. It was by far the most beneficial AEO session I’ve ever attended, each sharing tactics they’ve successfully used to enhance LLM visibility.

Here’s what they shared and what I’ve learned:

1. Advertorials work

I discovered that LLMs don’t currently differentiate between paid and organic editorial content. Well-placed advertorials on reputable sites can boost a brand’s visibility in AI search, similar to earned coverage. As with traditional PR, the publication’s credibility remains crucial.

2. Syndication can scale visibility

Paid syndication increases reach, but focusing on quality over quantity is essential. I learned to prioritize reputable and relevant publications when employing this tactic.

3. Map pages to every audience and use case you serve

By creating clearly defined pages for each audience, industry, and use case, I can better position my brand as AI search becomes more personalized. This structure assists LLMs in understanding relevance and remains a strong SEO strategy.

4. Homepage clarity

I ensure that my homepage clearly communicates who I serve and what I do. LLMs analyze homepage content more effectively than navigation menus, so relying on the latter alone is a missed opportunity.

5. Optimize your footer

I’ve started optimizing the footer of my site. As Wil Reynolds demonstrated in a compelling case study, LLMs pick up on brand and service signals located there, enhancing visibility.

6. Don’t prioritize llm.txt

Despite ongoing speculation, there’s been no confirmation from significant LLMs about the use of llm.txt files, and Google explicitly states they don’t. I focus my efforts elsewhere for better results.

7. Go multimodal

To improve brand recognition across multiple sources, I repurpose core content in various formats like text, video, audio, and imagery, maximizing the chances for LLMs to pick it up.

8. Actively shape your brand narrative

It’s estimated that 250 documents are needed to meaningfully influence an LLM’s perception of a brand. By consistently publishing and promoting content, I ensure that my brand narrative remains in my control.

9. Freshness carries disproportionate weight

Fresh content generally performs better in AI searches, reflecting LLMs’ preference for recent information. However, purely artificial “refreshing” without meaningful updates is not advisable.

10. Social works fast

Updates on platforms like LinkedIn, including Pulse articles, can appear in AI search within hours, sometimes minutes. Platforms with high trust like Reddit and YouTube display similar rapid visibility.

11. Authority accelerates inclusion

Publishing on respected, niche industry sites can lead to rapid inclusion in LLM responses, sometimes in mere hours.

12. Don’t hide FAQs

FAQs should be accessible and well-detailed, not concealed within accordions. Eight to ten well-addressed questions can effectively signal expertise, intent, and relevance to both users and LLMs.

Is AEO the same as SEO?

John Mueller from Google clarified at Google Search Live that AEO relies on SEO fundamentals: doing tricks may work short-term, but long-term success relies on proven stability.

The correlation is logical when considering modern LLMs like GPT-5, which utilizes Retrieval-Augmented Generation (RAG) to query real-time data. To gain LLM visibility, showing up in search results is essential.

For a deeper dive, Lily Ray’s excellent video is worth watching.

In essence, good AEO practices align with good SEO, though there’s nuance, and while these tactics are effective now, they will evolve as LLMs grow more sophisticated.

The best AI search strategy for 2026

Forget the magic button. Keep testing, remain skeptical about the hype, and be selective about the advisors you trust.

Thanks to Bernard Huang and Clearscope for hosting this insightful panel.

Inspired by this post on Search Engine Land.

January 29, 2026