Category: Content

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

Unlocking Google Discover: How Content is Ranked and Filtered
Through my recent dive into the latest SDK findings, I’ve discovered why some pages never make it to the Google Discover ranking. Factors like predicted click-through rates, images, and content recency are key drivers.

One thing I’ve learned is that Google Discover operates using a detailed, multi-layered pipeline. This includes publisher blocks, detailed image specifications, a freshness decay model, and extensive experimentation that shapes what appears on users’ feeds, as explained by SDK-level researcher Metehan Yesilyurt.

Why this matters to us. As someone who’s eager to drive significant traffic via Google Discover, I’ve often found the process unpredictable. This research allows me a clearer understanding of how my content might qualify, rank, or get blocked, shedding light on potential pitfalls before a piece even begins to rank.

The nitty-gritty. In Yesilyurt’s exploration, Google Discover’s app framework was deconstructed into a nine-stage process. Here’s how it works:
- It all begins with Google crawling and understanding the content I produce.
- It examines key meta tags, such as image and title.
- It classifies content types, be they breaking news or evergreen material.
- Google checks if my content is blocked at any point.
- Content is then matched to user interests.
- An applied server-side click-through rate prediction model comes into play.
- The feed layout is constructed based on these evaluations.
- Content is served to users, inviting engagement.
- Lastly, user feedback is recorded.
A significant insight. One crucial discovery is that publisher-level blocks occur before matching content to users’ interests. A user’s decision to block a source means my content won’t even make it to the ranking stage.
- Such blocks are impactful. A single action to prevent showing content from my site can suppress the entire domain. Unfortunately, no similar sitewide boost exists.
The ranking mechanics. The ranking process leverages elements like my content’s title, image quality, and past engagement history. Google’s servers use a predicted click-through rate (pCTR) to estimate the possibility of clicks. Although the specific model remains unseen, the app indicates which signals Google considers for ranking, including:
- The page title, sourced from og:title.
- The size and quality of images.
- The freshness of the content.
- Past click and impression statistics for my URL.
- Whether images load correctly on the page.
The importance of freshness. Google’s system groups content based on age:
- 1 to 7 days old: enjoys the strongest boost.
- 8 to 14 days old: retains moderate visibility.
- 15 to 30 days old: sees a drop in visibility.
- Over 30 days old: experiences a gradual decline.
While evergreen content might receive special classification, newer content inherently gains an edge.

Image and meta tag criteria. Google Discover examines six key tags at the page level, such as og:image and og:title. Notably, missing images result in the absence of content cards.
- Images must be at least 1200px wide for prominent card features. Smaller images often manifest as thumbnails, which typically receive fewer clicks.
- Missing tags prompt Google to seek alternatives — if og:title lacks, the Twitter title tag or HTML title might be used instead.
- Using meta tags like “nopagereadaloud” and “notranslate” can prevent a page from appearing on Google Discover altogether.
The personalization factors. With Google Discover, personalization hinges on:
- Google’s broader interest data interconnected with user behavior.
- Publisher signals, which include registration with Publisher Center.
- Personal interactions like follows, saves, and story dismissals.
- Engagement metrics, like the time users spend reading content.
If a reader dismisses my content, that action is stored permanently for that specific URL, preventing it from reappearing.

Everywhere I look, experiments abound. During moments of observation, about 150 server-side tests were simultaneously active, with an additional 50+ features controlling how content cards were depicted.
- This means two users with similar interests can encounter vastly different feeds simply due to being in different experimental groups.
Real-time updates for your feed. Google Discover doesn’t stand still. It can dynamically add, remove, or reorder content in the feed as a user scrolls, no refresh needed.

Key insights for success. Excelling in Google Discover is less about using tricks and more about meeting eligibility criteria, establishing trust, utilizing compelling visuals, and maintaining engagement, especially in a system capable of filtering content before the ranking process even starts.
- Publisher blocks occur before any ranking.
- The system inherently values content freshness.
- High-quality images and clear titles are indispensable.
- User dismissals are long-term.
- Heavy experimentation leads to a constantly evolving environment.
The research I’ve examined can be found here: Google Discover Architecture: Clusters, Classifiers, OG Tags, NAIADES – What SDK Telemetry Reveals

Inspired by this post on Search Engine Land.
February 25, 2026
Mastering Fresh Content: Stand Out in an AI-Driven World

I’ve come to realize that AI has dramatically simplified the publishing process, but it also means standing out amidst the noise is increasingly challenging. The good news is, by focusing on clarity, intent alignment, and a few strategic SEO adjustments, we can make significant progress.

As AI breaks down the barriers to production, the web is getting flooded with content that is polished, optimized, but often lacks distinctiveness. When everything seems competent, you and I must strive harder to differentiate our voices.

Though AI has transformed how content is churned out, the core of what users seek—intent—remains unchanged. They sift through headlines and descriptions, rewarding clarity and effectiveness. This is why foundational elements matter even more now.

I find that keeping content fresh isn’t about being novel for novelty’s sake. It’s about diving back into what makes content truly unique: distinct messaging, structured delivery, and a deep grasp of our audience’s needs.

The Real Problem with AI Content

The crux of the issue with AI-generated content isn’t its factualness—it’s its sameness. AI draws from vast pools of existing content, often reproducing unremarkable tropes and conclusions. Individually, they seem fine; collectively, they’re indistinguishable.

This homogeneity is why so much content today feels the same. Even when relevant, it seldom provides a unique reading experience.

Both users and search engines are responding in kind. In a sea of similar content, differentiation becomes key. At this juncture, originality, specificity, and intent alignment have taken on heightened importance.

Ironically enough, AI has increased the value of originality. As automated content inundates the web, signals like clarity, usefulness, and intent alignment become beacons of high-quality content.

Many teams falter here, competing with AI by focusing on quantity over quality. Freshness isn’t about novelty; it’s about crafting content that feels distinctly human and undeniably helpful.

Fresh, Unique Content is Still Built on Classic SEO Principles

Ever since content creation tools evolved, what’s been constant is how people interact with search engines. Users still show up with an issue to solve, skimming through results to pick what seems most relevant.

Despite the rise of AI, this behavior endures.

Page titles, headings, and meta descriptions serve as that crucial first contact with the user. They function almost like ad copy, contrary to assumptions that these elements are becoming obsolete.

Classic SEO principles—clear search intent alignment, descriptive language, organized structure—continue to underpin fresh content.

Although these aren’t groundbreaking ideas, their importance has surged. A tweak in clarity doesn’t just help search engines index a page; it helps users find answers to their questions.

Small SEO Changes Can Lead to a Strong Impact

A recent experiment on my website examined whether more descriptive titles could boost clicks without altering the underlying content. We tested the hypothesis by aligning page titles more closely with search intent and user needs.

The result? A greater alignment led to a substantial increase in click-through rates, proving that small changes can powerfully impact visibility and engagement.

Strategies for Keeping Content Fresh in an AI-Saturated World

Remaining fresh in the AI era isn’t about jumping on every new tool but requires intentionality in creating, positioning, and maintaining content.

1. Treat Intent as Strategy

The essence of SEO has always been search intent, not keyword stuffing. Before crafting content, ask what problem the searcher is trying to address and what a good answer would look like in their context.

2. Use Page Titles and Headlines as Tools

In a crowded SERP, an effective title is crucial to catch a user’s attention and make them click.

3. Refresh Before You Create

Oft-overlooked is the power of improving existing content. You don’t need to produce new content incessantly when updates can achieve better results.

4. Lean into Specificity and Constraints

While AI excels at general advice, human-guided content shines through specificity and context, offering expert insights and breaking down misconceptions.

5. Use AI as an Accelerator

AI should accelerate tasks that don’t require judgment. Editorial responsibilities still lie with us, ensuring content aligns with our goals.

6. Measure Freshness by Behavior

It’s not the volume of content but engagement metrics like time on page and scroll depth that define freshness.

7. Accept that ‘Traditional’ Doesn’t Mean Outdated

Mainstays like clarity, structure, and relevance have only gained importance in our AI-driven landscape.

Why Fresh Content Actually Wins

While AI has revolutionized content speed and accessibility, truly effective content remains appealing and relevant, aligning with users’ search intent and preferences.

Inspired by this post on Search Engine Land.

February 24, 2026
Boost SEO: Mastering Content Tools for Google’s Initial Retrieval
I often find myself over-crediting Google’s understanding of my web pages. It’s easy to imagine Google as an AI wizard that fully comprehends nuances, expertise, and quality. Yet, during the DOJ antitrust trial, I learned something intriguing.

Google’s VP of Search, Pandu Nayak, testified about a first-stage retrieval system that relies heavily on word matching, rather than any magical AI trick. The foundation is based on older information retrieval techniques, like inverted indexes and postings lists. Okapi BM25, a well-known lexical retrieval algorithm, was cited as a crucial link in Google’s system evolution.

After this initial stage, which is all about word matching, Google employs advanced AI models like BERT on a smaller set of content. These content tools are key to optimizing documents for this stage, yet many use them incorrectly, despite their real value.

In this exploration, I’ll dive into the mechanics of first-stage retrieval, its significance, what content tools actually reveal, and how to effectively use these tools to get noticed by Google without obsessing over perfect scores.

How first-stage retrieval works and why content tools map to it

Understanding BM25 is essential. This retrieval function, crucial to Google’s first-stage system, prioritizes topicality by scanning vast amounts of data quickly, narrowing candidates for further processing.

And for me, as a content creator, certain details stood out.
- Term frequency with saturation: At some point, repeating keywords has diminishing returns.
- Inverse document frequency: Less common terms score higher, so specificity is rewarded.
- Document length normalization: Longer documents can be penalized, as density matters.
- The zero-score cliff: Not mentioning a term means zero visibility for related queries.
So, effectively using these tools means identifying gaps in my content and ensuring relevant terms appear. Tools like Surfer SEO and Clearscope guide me in avoiding the zero-score pitfall, offering significant value.

AI enhancements like RankEmbed can assist, but counting on them to fill vocabulary gaps is a gamble. I focus on ensuring my core content is strong at the first retrieval stage.

What the research on content tools actually shows

Research shows a weak-positive correlation between content tool scores and rankings, with studies yielding a 0.10 to 0.32 range. While meaningful, these findings are often derived from studies conducted by vendors using their own tools.

The real test remains: do these tools help a new page climb in rankings? The consistent finding is their efficacy in positioning content for retrieval, not securing high rankings against competitors.

Why not skip these tools altogether?

It’s a mistake to write off these tools, especially since expert writers, myself included, often use overly technical language that audiences may not search for or understand, a classic example of the “curse of knowledge.”

A real-world example is Clearscope helping Algolia align their language with their audience’s searches, ultimately lifting their content’s page ranking significantly.

By showing me what vocabulary is used by successful pages, content tools reduce hours of analysis to minutes, whether I’m a frequent publisher or a solo blogger.

What about AI-powered retrieval?

Dense vector embeddings power AI retrieval but supplement rather than replace word matching due to computational limits. Hybrid systems combining traditional and AI search techniques consistently perform best.

The takeaway for me is clear: AI matters, but traditional retrieval carries significant weight and serves as the foundation of effective content scoring tools.

How to actually use content scoring tools

Common advice tells me to get high scores with tools like Surfer SEO or Clearscope. However, I focus on using them wisely to target the zero-score terms and refine competitor analysis.

Running these tools during research, not during writing, ensures I remain focused on quality and audience relevance rather than just scoring high numbers.

A note on entities

Google’s Knowledge Graph processes the relationships between entities more deeply than most tools measure. Recognizing the gap between flat keyword lists and Google’s more complex understanding helps me focus on providing detailed context.

Retrieval before ranking

Content tools effectively decode retrieval stage vocabulary, a less sensational, but fundamentally honest function. They help me pass the first stage of Google’s pipeline, setting the stage for engaging with more advanced ranking factors later on.

Inspired by this post on Search Engine Land.
February 23, 2026
Unlock Media Exposure with this Proven Press Release Strategy
Are you looking to amplify the reach of your next press release? Employ this innovative framework to transform your announcements into exceptional successes for your clients.

I had given up on press releases years ago, convinced they had lost their impact. But a conversation with a trusted friend and mentor totally shifted my viewpoint.

She revealed that while the days of organic features from merely publishing a press release were over, great results were still attainable. Her secret? She effectively pitched relevant journalists, using the press release’s key points as leverage once it went live.

Skeptically, I gave her strategy a shot. The results were incredible, leading to multiple organic features for my client.

My immediate thought was, “If such a minor tweak yielded these results, imagine the possibilities with a full-fledged strategy.”

This method I’m about to share is the culmination of a year packed with trials and enhancements to amplify the efficacy of my press releases.

Although it demands more research, planning, and execution, the pay-off is exponential and undoubtedly justifies the additional effort.

Research Phase

You’ll start with what your client wants to communicate to the world. Here’s how to proceed:
- Identify related topics like economic impact, related technology, legislation, and key industry players.
- Locate media coverage in the past quarter on these topics in outlets where you’d like your client featured.
  - Compile a list with links to each article, its main points, the journalist’s contact information, and links to related social media posts they’ve shared.
- Organize the list by how closely it aligns with your client’s message.
Planning Phase

Draft your client’s press release, using opportunities to cite articles from your compiled list with relevant links.

Ensure each citation is relevant and adds value to your message. Aim for three to five citations to maintain focus.

Simultaneously, create personalized pitches to the journalists whose articles you’re citing, ensuring they align with their beat and previous coverage.

Briefly mention their past work — a short, recognizable quote suffices. Include links to current social media discussions showcasing interest in the topic. Conclude with your press release link and a specific call to action.

Avoid trying to win favor through citations. Instead, illustrate the link between your client’s message and their prior coverage, making it easier for journalists to revisit the topic from a fresh angle.

Execution Phase

Initially, interact with journalists on your list via social media for several days. Comment on recent posts, especially those covering your target topics. This starts building name recognition and rapport.

Once your press release is published, promptly send your pitches to the three to five journalists you cited, including the live release link. (I recommend linking to the most credible syndication rather than the wire service version.)

Subsequently, approach other pertinent journalists, customizing each pitch with relevant points from their past articles that align with your client’s message.

Track all earned organic features. While some may emerge from the press release publication itself, more commonly, they result from direct pitches, opening new doors for visibility.

Review each new feature for references to other articles from your compiled list. Then approach the original article’s journalist, referencing the new piece that relates to or enhances their work.

The Psychology Behind Why This Works

This strategy taps into two potent psychological principles:
- Everyone likes to see their work acknowledged, validating their viewpoint in the process.
- Building on a previously covered topic is less labor-intensive than starting from zero, appealing to journalists’ needs to streamline their work.
This framework will elevate your next press release, garnering more media coverage, increasing client satisfaction, and achieving impactful results with minimal effort — truly shining as a professional.

Inspired by this post on Search Engine Land.
February 20, 2026
Mastering AI Visibility: Applying ‘They Ask, You Answer’

As someone who’s deeply involved in SEO, I’ve noticed how search behavior has evolved significantly. It’s not just about typing keywords into Google anymore. People are asking questions, and sometimes, they’re even outsourcing their thinking to Large Language Models (LLMs).

With Google transitions from a traditional search engine to more of a question-and-answer machine, it’s crucial for businesses to have a robust and time-tested strategy to respond to these customer inquiries.

AI has transformed how we research and compare options — making what used to be a painstaking process much simpler. But the machine only knows what it can discover about us online.

To achieve the broadest visibility for your business, it’s vital to understand your customers’ needs, desires, and pain points thoroughly.

This is where the “They Ask, You Answer” framework becomes invaluable. It assists businesses in identifying and formulating answers to the numerous questions potential customers might have. In the age of AI, this approach is not just useful but essential to making progress.

An Answer-First Strategy and Its Importance Now

“They Ask, You Answer,” crafted by Marcus Sheridan, is more than just a book — it’s a strategic shift. I highly recommend diving into it.

The premise is straightforward: buyers have questions that businesses should address candidly and transparently. Avoid burying leads with vague responses like “Contact us for a quote.”

This isn’t merely an inbound marketing strategy but a practical extension of your customer-facing content with an E-E-A-T focus.

The framework includes five essential content categories: Pricing and cost, problems, versus and comparisons, reviews, and best in class.

These align with the key moments buyers experience in seeking solutions, assessing risks, and making decisions. Nowadays, many of these interactions happen in AI environments, making the TAYA process particularly relevant.

The modern web can be overwhelming with its chaos and distractions. AI steps in to simplify this — providing a clean, orderly way to find information. This is why TAYA, with its question-and-answer foundation, works so seamlessly with AI systems.

Your customers are searching everywhere, so it’s crucial to ensure they can find your brand.

Transforming E-E-A-T into a Practical Strategy

Although we have E-E-A-T as an ideal for content creation, effectively building a strategy around it can be challenging. “They Ask, You Answer” places this focus on tracks.

E-E-A-T categories: Pricing supports trust, experience, and expertise. Problems demonstrate experience. Versus content builds authority and expertise. Reviews enhance experience and trust. Best-in-class content fortifies authority and trust.

Building trust through E-E-A-T might be complex given the myriad ways to exhibit it. TAYA helps organize these signals within each category, creating a comprehensive repository of content that AI readily surfaces.

Ready to dig deeper? Discover how to build an effective content strategy for 2026.

Integrating TAYA with Traditional SEO Research

Drawing from our SEO skills and tools positions us strongly in the AI era. These resources aid in forming an integrated SEO, PPC, and AI strategy.

The action plan includes using Google Search Console, Google Business Profile, semantic maps from tools like AnswerThePublic, and competitive analysis with Semrush or Ahrefs to identify unique opportunities.

Explore your internal resources: Sales calls, live chat transcripts, emails, and customer feedback can reveal valuable insights.

This understanding allows us to collect and categorize questions under the TAYA framework.

TAYA and Your AI-Era Content Marketing Strategy

Here’s what TAYA looks like reinterpreted for an AI-driven landscape where Google and other systems anticipate user needs.

1. Pricing and Cost: Why Discussing Money Matters

Clarity on pricing helps potential buyers in their decision-making process. If businesses don’t provide detailed, transparent information, AI will present whatever it finds, which might not reflect your brand accurately.

To own this narrative, I recommend publishing price ranges, elaborating on cost-driving factors, and setting transparent expectations.

2. Problems: Leveraging Weaknesses as Strengths

Being candid about drawbacks and limitations fosters trust. Acknowledge potential issues constructively to reinforce credibility.

Craft content that addresses these issues head-on, providing practical advice and solutions.

3. Versus and Comparisons

Comparisons help simplify decision-making by highlighting differences clearly. Ensuring that your content reflects this can help in establishing your brand as a reliable source.

Focus on creating structured, easy-to-digest comparisons that guide potential buyers through their options.

4. Reviews and Credibility

This isn’t just about gathering positive reviews but creating genuine, review-like content to assist in evaluating options.

Offer honest evaluations and showcase your first-hand experiences to stand out as a truthful source.

5. Best in Class: Recommending Others at Times

Sometimes, acknowledging that another service might be best for certain needs builds trust. People appreciate honesty, enhancing your credibility as a fair evaluator.

Creating comprehensive and unbiased “best of” lists based on transparent criteria can place your brand as a trusted advisor.

TAYA as the Guide for Answer-First Visibility

In AI-driven content marketing, middle-of-the-funnel content plays a pivotal role. Your website retains its foundational importance as SEO remains crucial for AI visibility.

Using TAYA as a map empowers you to create a strategy that ensures presence across the AI spectrum. Each piece of content should respond to a real buyer question, emphasizing decision-stage content over mere branding awareness.

With AI and SEO, success is measured beyond clicks. It’s about becoming a trusted source and cementing the relationship with potential customers through quality content.

Inspired by this post on Search Engine Land.

February 18, 2026
Mastering Persona GPTs: Boost Your SEO with Targeted Insights
In my ideal world, reaching out to a top customer for feedback on a piece of content would be a breeze. However, the reality is often different—conducting audience interviews can be both challenging and time-consuming, especially when I’m crafting a new topic or refining an existing one.

A few years back, content marketing was a simpler game—just focus on keyword intent and excellent content to capture clicks from Google’s top search results. Now, in the AI-driven era, the stakes and expectations have evolved significantly.

Audience research has now become a non-negotiable aspect of my strategy. Sadly, not every company has the resources to carry it out effectively.

To bridge this gap, I’ve learned to create custom GPTs in ChatGPT that draw on my persona research. While these don’t entirely replace traditional audience research methods, they certainly help me pinpoint gaps or discrepancies in my content quickly.

Let me share how GPTs work, so you, too, can employ them for audience research.

Perform Audience Research

With the SEO scene constantly shifting, audience research is my strongest ally in understanding the motivations behind search intent.

Here’s a rundown of some intuitive methods and tools I’ve found useful for getting started with research:
- SparkToro: By exploring websites, interests, or specific URLs, I can segment audience types, whether I’m looking for an overview or diving deeper.
- Review Mining: I use various tools to automate the scraping of reviews about my company or competitors, which I then analyze to understand customer likes, dislikes, and their reasons.
- Listening to Calls/Review Leads: An invaluable resource, listening to customer interactions with my sales team gives me real-time insight into their questions and what prompted their calls.
Dig deeper: How to do audience research for SEO

Create a Customer Persona

After completing my research, I build a persona to represent my target audience. Tools like Figma and FigJam are invaluable for this task.

The personas I create include:
- Names, biographies, and trait sliders.
- Their interests, influences, goals, and pain points.
- User stories and emotional journeys.
- Content focus, trigger words, and calls to action (CTAs).
- Complete customer journey steps.
- Supporting data from reviews.
Create a Custom GPT of Your Persona

With my persona research complete, I proceed to create a GPT. Here’s how I do it step-by-step:

I start by logging into ChatGPT and heading to Explore GPTs from the sidebar. In the corner, I click on Create.

There, I prompt ChatGPT using the data from my audience research, sometimes embedding screenshots for clarity.

Once the GPT is set up, I can engage with it under the Configure tab, using conversation starters to explore changes and updates.

Although these GPTs aren’t perfect substitutes for real-life audience surveys, they provide swift feedback on content alignment and gaps.

For instance, GPT “Hank” assisted in refining an above-the-fold section to ensure it met its intended goal.

While I don’t always take “Hank’s” advice word for word, his feedback is invaluable when time is of the essence.

Dig deeper: 7 custom GPT ideas to automate SEO workflows

Ensure Data from Your GPT is Accurate

AI-generated analysis isn’t definitive. If you’re skeptical of GPT’s accuracy, confirm its claims by checking the evidence drawn from the data you provided.

The GPT can revise itself when errors are found; just ask for corroboration from the persona data.

Update Your Persona-Based GPT

My GPT is never static. I enhance it with more data for greater effectiveness.

Returning to ChatGPT’s Explore GPTs, I access My GPTs to update my persona.

By clicking on Configure, I can add, adjust, or remove persona details. This constant updating ensures relevance as I learn more about my audience.

A persona is always evolving, so the more I learn, the better my GPT becomes.

Leverage Persona GPTs for SEO Content

Though not foolproof, GPTs and AI-generated personas are helpful allies in optimizing content.

Once comfortable, I begin creating personas for wider audiences, niche segments, or particular campaigns.

In the ever-shifting landscape of SEO and marketing, I can’t afford to be complacent. As audience insights and intentions evolve, I ensure my GPT remains relevant by updating and pruning irrelevant details.

When used correctly, these tools are powerful companions to SEO efforts, channeling traffic and boosting conversions.

Inspired by this post on Search Engine Land.
February 17, 2026
Boost Your AEO Strategy with New Contentful Integration

I’m thrilled to share that Profound Agents now offer direct integration with Contentful CMS. This integration brings native Contentful support right to your AEO automation stack, enhancing your strategy and capabilities.
With this development, I’m sure you’ll find managing content and automations far more streamlined and efficient. Having the power of Contentful within reach means we can align more closely with modern content management needs.
I’m eager to see how this integration will open up new avenues for optimizing our automated processes and elevating overall performance.

Inspired by this post on Try Profound Blog.

February 17, 2026
Unlock AEO Success with Content Siloing: Boost Authority & Crawlability

Do you want to take your Answer Engine Optimization (AEO) to the next level? Content siloing might just be the strategy you need. It’s a tactic that has transformed how I approach structuring topics to enhance authority and improve crawlability. Let’s delve into what content siloing is and how you can successfully implement it to boost AI citations.

Think of content siloing as creating a tightly knit topic network within your website, where each piece of content supports and strengthens the others. By organizing related content into isolated ‘silos,’ you not only streamline user navigation but also make it easier for search engines to index and understand the relevance of your content. This improved visibility can lead to better ranking in AI-powered search results.

Implementing content siloing involves a strategic approach to linking content. Begin by identifying your core topics and create subtopics that branch off these main areas. Each article within a silo should link to related content, reinforcing the overall theme and strengthening your site’s authority on the subject matter. This method ensures that your website becomes a trusted source of information in the eyes of both users and search algorithms.

Inspired by this post on HiGoodie Blog.

February 12, 2026
Enhance Your Strategy with Profound Knowledge Base

When I upload documents to the Knowledge Base, I provide Profound Agents with a comprehensive, single source of truth about my company’s unique information. This ensures that every marketing action performed on my behalf is informed with the right context about my brand.

Inspired by this post on Try Profound Blog.

February 11, 2026