Tag: Content Optimization

My AI Content Gap Workflow for Smarter SEO Priorities
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.

That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.

Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.

My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.

I bring my SEO data together before analyzing it

In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.

I follow this process in one of two ways.
- I export reports directly from the platforms and upload them to Claude.
- If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.

Step 1: I choose the right competitors

A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.

If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.

I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.

I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.

I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.

There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.

A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.

Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.

Once I have settled on the right competitors, I am ready to find the gaps that matter most.

Step 2: I gather and prepare the data

With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.

I like to pull data from three core sources.

Semrush: I find the gaps

I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.

From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.

The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.

Google Search Console: I validate the opportunity

Next, I check Google Search Console before assuming every missing keyword deserves a new page.

For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.

Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.

In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.

Google Analytics: I add business context

Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?

A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.

I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.

If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.

I clean the data before handing it to Claude

If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.

I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.

For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.

Step 3: I ask Claude to find the story in the data

At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.

This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.

The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.

Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.

Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.

Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.

For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.

A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.

Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.

I separate quick wins from long-term investments

Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.

Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.

This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.

I do not skip the human review

Claude can organize information remarkably well, but it does not know the business the way I do.

Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.

If the answer is no, I refine the cluster or remove it.

The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.

The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.

Step 4: I score and prioritize the opportunities

Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.

This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.

I score each opportunity across several criteria before I build a roadmap.

A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.

Business relevance

I start with a simple question: if this content performs well, does it help the business?

Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.

Existing authority

Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.

In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.

Search demand

Search volume matters, but I do not let it dominate the scoring model.

A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.

Ranking difficulty

I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.

This quick reality check keeps me from chasing opportunities that are not practical.

Estimated effort

Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.

Both can be worthwhile, but they should not carry the same priority when resources are limited.

I let Claude apply the framework

Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.

For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.

A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.

The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.

By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.

Step 5: I turn priorities into page-level recommendations

Once I have prioritized the opportunities, the next step is figuring out exactly what to change.

Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.

Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.

For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.

One of the biggest advantages of this approach is validation.

Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.

Catching those issues before assigning work can save hours of unnecessary effort.

The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.

I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.

Step 6: I measure whether the gap is closing

Publishing the content is not the finish line. It is the start of the next round of analysis.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.

Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.

If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.

Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.

I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.

The tools will continue to improve, but the repeatable workflow is what creates the advantage.

I build a repeatable content gap analysis process

A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.

Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.

Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.

The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.

Prompt template: My prioritized content gap roadmap

Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.

“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:
– Industry:
– Products/services:
– Target audience:
– Primary business goals:
– Geographic focus:
– Any strategic priorities or constraints:
– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories:
– Competitors rank and we don’t.
– We rank below competitors.
– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine:
– Which topics already receive impressions.
– Which pages rank between positions 8 and 20.
– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:
– Which pages drive meaningful engagement.
– Which pages contribute to conversions.
– Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by:
– Search intent
– Business relevance
– Funnel stage
– Recommended content type
– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using:
– Business relevance
– Existing authority
– Search demand
– Ranking difficulty
– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:
– Quick wins
– New content opportunities
– Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should:
– Refresh an existing page
– Consolidate multiple pages
– Create a new page
– Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create:
– An executive summary
– Prioritized topic clusters
– A scored opportunity table
– Page-level recommendations for the highest-priority URLs
– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”

This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.

Inspired by this post on Search Engine Land.
July 8, 2026
Remembering Bruce Clay: SEO Pioneer’s Final Lessons

My heart sank when I learned that Bruce Clay had passed away. I knew he had been in the hospital, but my mind went straight to the two long conversations we had last fall: one simply to catch up, and one for what would become a deeply meaningful podcast interview.

I first reached out to Bruce nearly 25 years ago. I had emailed him cold to ask whether I could republish some of his industry writing about ethics. He said yes. Somehow, the article I cited unintentionally ranked No. 2 on Google for “Bruce Clay” for years. I joked with him about that more than once, and he always seemed both amused and slightly annoyed, probably because I had done it with his own content and his own blessing.

A few years later, I worked with Bruce and many other search professionals on the board of the Search Engine Marketing Professionals Organization, better known as SEMPO. It was a business nonprofit built to support and legitimize the then-new search industry. We promoted best practices, helped make the business case for search, and later became involved in U.S. Internet policy work in the early 2010s.

SEMPO brought together board members from around the world, and in a very literal way, it took some of us around the world. That work is where I really got to know Bruce. Later, we would run into each other at conferences, sometimes even on the same panels. We were doing serious work, but we also had a great time doing it. The organization lasted about 15 years, and if I remember correctly, Bruce was one of its founding members around 2000 or 2001.

One memory of Bruce has stayed with me vividly. A group of us from the SEMPO board were walking back to our hotel on the east side of Midtown Manhattan after dinner. A snowstorm had just begun, one that would leave several feet of snow by the next day. The usual roar of traffic had been softened by the weather and the empty streets. It was eerie, but almost joyously quiet. The city that never sleeps seemed to be taking a nap under a blanket of snow.

Then something happened that I had never seen before, and have never seen since.

As snow poured silently into the streets, a massive lightning strike hit just a few blocks away, over Bruce’s shoulder. I do not know whether he saw it directly. It felt like an explosion. We stood there for several minutes trying to understand the contrast: a shattering bolt of lightning between skyscrapers, in the middle of a torrent of snowflakes, with not a drop of rain.

None of us knew what to call it. I believe Bruce called it “thunder snow,” and the name stuck. In that moment, his naming streak continued.

Bruce was, and remains, the real deal in search. His legacy was never only about coining a term. He pushed the field forward, taught others generously, and stayed deeply connected to the people he cared about. Like many of the earliest professionals in search, he helped shape practices that still feel foundational today. Through his writing, interviews, books, tools, and hundreds of industry events, he became one of the people the industry looked to for clarity. For many who remember the beginning, and for many who still followed him closely, Bruce was the GOAT.

I always felt that Bruce approached search intellectually. I do not think he saw it only as a job. It was exciting, unfinished, and new. Very few people get to help invent an entirely new discipline, and Bruce understood what that meant. He also recognized that AI is one of those moments now, and he approached it with the same curiosity, energy, and insight he brought to early search. Many people in the industry may only now be realizing that Bruce pioneered things they do every day. They feel obvious now, but they were not obvious then. Even the basics had to be debated and established.

He was not only passionate about search. He was passionate and generous toward the people in search. If you cared about the work, you were part of his tribe. That was true for thousands of people in the industry, myself included.

With Bruce, I could get deep into the weeds of the trade and still talk broadly about where everything was headed. He was an engineer with an MBA, and that combination came through in his leadership, expertise, and authority. He understood the work from top to bottom, and then back to the top again.

He was also genuinely kind. He had friends around the world. In our last conversations, I sensed that he was content with his life and accomplishments, and that he felt blessed by the path life had given him. He had nothing left to prove.

In the podcast interview, Bruce was as sharp and insightful as ever. He offered some of the most sensible thinking I have heard about where search is going in the world of LLMs. He was still innovating, just as he had been when search first began taking shape nearly 30 years ago.

Because search is so closely tied to language, I have been especially interested in how we think about, and what we call, this “new” thing. Bruce’s perspective helped crystallize my own research. Over the last year, I have watched much of the industry move toward the same conclusion he shared in our discussion.

If you are one of the many thousands of people who talked shop with Bruce over the years, I think you will recognize him in the ideas that follow. You may even relive some of your own conversations with him.

As I reviewed the podcast transcript, I realized we had recorded hours of conversation beyond search, including cars and all kinds of other subjects. At the end of our first conversation, he said goodbye with great love and care. That was Bruce. Those words land differently with me now, and they always will.

Rest in peace, Bruce. I miss you already.

What Bruce taught me in our final industry conversation

When I asked Bruce to talk about how he got started in the 1990s, he took us back to 1996. He had been working in corporate roles and wanted to become a consultant. His background was in math, programming, mainframes, PCs, networking, and optimization. When the Internet began moving into the mainstream, he saw something that matched both sides of his skill set: marketing and technical work.

He started studying search engines because that was where the opportunity was. He experimented with what they wanted, adjusted web pages, and watched rankings appear. Then people began calling him and paying him. What he thought might become a one-person consulting business grew quickly into something global, with offices and work across Japan, Australia, Asia, Europe, India, and beyond. Bruce told me he never would have predicted it would take off the way it did.

I reminded him how small the field was in those days. There were literally only tens of people doing this early on. Bruce was one of the first to build a legitimate service for businesses that needed to rank for their own brand names and for broader generic terms, while other corners of the field were still experimenting with black-hat tactics.

Bruce pointed out that this was three years before Google. Search was a wild west. There were more than 20 major search engines, and many of them were taking data from one another. At the first SEO conference he remembered attending, all of the leading people in the field sat together at one round table in a bar. He joked that if a natural disaster had happened there, the whole industry might have disappeared.

We talked about Danny Sullivan, Search Engine Watch, Search Engine Strategies, and the early vocabulary of the industry. Bruce had long been credited with helping coin the term “SEO,” though he was careful to say that no one can know who said something first. What he did know was that only a handful of people were in the room when the term started to take hold.

At the time, other terms were in play, including “search engine positioning” and “ranking.” Bruce believed “optimization” won because it sounded technical, valuable, and precise. It was like fine-tuning a race engine. People could see themselves building a profession around it. Once the industry attached itself to that word, the term spread quickly around the world.

That led us into the newer terms now being proposed around AI, including AIO, GEO, and AEO. I have been writing about how many of these terms still depend on the word “optimization.” Bruce’s view was clear: search engine optimization was never limited to organic blue links. It was about optimizing for anything a search engine produces that can drive business and traffic.

In Bruce’s view, if AI appears inside search and influences discovery, citations, visibility, or traffic, then it belongs under SEO. GEO and AIO were not separate disciplines to him. They were extensions, just like link building or on-page optimization. He warned that many new terms are marketing labels more than practical new fields. If the work required to appear in AI results is still mentions, links, schema, authority, content structure, and rankings, then the work is still SEO.

That point stayed with me. Bruce said that if someone claims you no longer need SEO and only need AI optimization, you should watch closely, because either they are going to do SEO under a different name or they do not understand what they are doing. He believed ranking in AI was possible, but the method was deeper and more complex than traditional SEO. To him, it was still SEO, just several levels more advanced.

We also discussed whether AI feels like search did in the late 1990s. Bruce believed it does in important ways. AI depends heavily on search engines because search engines have spent decades fighting spam and building trust signals. AI systems do not yet have that same history, so they rely on what search engines have already learned to filter, evaluate, and rank.

Bruce also believed AI could still be gamed at the content level. If enough pages repeat a false idea, an AI system may begin to treat it as true. He had already seen examples of people trying to influence AI answers by placing their names into “best SEO” lists across enough sources. To him, this was a sign that AI would need its own version of the spam fight search engines have been having for decades.

One of the most important parts of our conversation was Bruce’s explanation of Google AI Mode and how it changes the way SEOs should think about structure. He described how a query can produce an overview, followed by sections and subsections that allow users to drill into narrower parts of a topic. When a user clicks into a section, the supporting sites can change to match that specific subtopic.

That means content cannot simply be built around one broad keyword anymore. Bruce believed pages need to be structured so each section can stand on its own as an expert answer. A page should support a topic, but every H2-level section may need its own clarity, completeness, and internal logic. In his view, this raises the importance of siloing across a site and within a page.

I framed this as a shift from keyword-led thinking to context-led thinking. Bruce agreed and connected it to entities, fan-outs, references, and cross-links. Keywords helped build the industry, but he believed the future depends on understanding entities in context. If content cannot answer the question clearly, it fails the core purpose of AI-assisted search.

Bruce described the long-term target as something like the Star Trek computer: no matter what question someone asks, the system provides the answer. We are not there yet, but that is the direction. For websites, he believed the future architecture is question-centered, highly usable, structured into sub-silos, and able to answer and refer within a page while also fanning out to supporting pages.

That naturally led us to content. Bruce said that for years SEO treated content like a stepchild, but now content is a peer. If SEO teams and content teams do not share the same goal, they will keep writing the way they did 20 years ago and fail in the AI search environment. He was already being hired to train content teams, even though he did not consider himself a “content guy” in the traditional sense.

He believed the industry still suffers because SEO and content do not cross-pollinate enough. Content marketers may not attend SEO conferences, and SEOs may not spend enough time learning how content teams actually work. That separation matters more now because the structure of a page, the expertise of each section, and the way a topic is divided all affect visibility in AI-driven search experiences.

Bruce’s advice was direct: stop spreading one keyword across a page and calling that optimization. Instead, build each section as if it were a standalone expert answer. If the sections belong to the same theme, they should support one another, but each needs to carry its own weight. In his words, the hierarchy is no longer only the page. The hierarchy is also the section of the page.

When I asked Bruce about AI-generated content, he made an important distinction. AI is a tool, not a solution. He did not believe businesses should simply generate content, read it once, and publish it. Detection tools are inconsistent, and search engines may not reliably identify every AI-generated page. But that does not make low-effort AI content a good strategy.

Bruce believed AI is strongest as a research assistant. His own Pre-Writer product was built around that idea: gather deep research and give a human writer a stronger starting point. The writer still finishes the work, adds style, voice, judgment, compliance, and business understanding. For Bruce, reducing a four- or five-hour writing project to two hours was a win. Replacing the writer entirely was not.

He was especially clear that writers are artists. AI does not know a business the way its people do, and it does not bring the same finesse or judgment. The future, in Bruce’s view, requires writers, SEOs, and AI workflows to be integrated around shared goals. Without that maturity, teams will keep producing pages that look like they were built for search 10 years ago, and those pages will be ignored.

We ended by talking about tools. Bruce reminded me that in the beginning, he wrote tools because none existed. He built one of the first page analyzers, including what he once called a keyword density analyzer. He later received a patent related to that kind of technology. His tools were never meant to replace large platforms like Semrush, Ahrefs, or Surfer. They were meant to extend them by analyzing things those platforms did not.

Bruce pointed people to seotools.com and described the tools as inexpensive power tools, not products designed for the masses. Some users did not understand them at first, but came back later when they saw the value. He was still building, still solving problems, and still thinking about what the industry needed next.

Near the end, Bruce mentioned a newer tool designed to show traffic loss through Search Console data over time, helping site owners see whether they had fallen off a cliff or declined gradually. It struck me as classic Bruce: while others complained that something should exist, he was building it.

I thanked him for the conversation, and he answered with warmth: he was glad I had him on, and he loved talking with me. I hear those words differently now. I am grateful we had that final conversation, and I am grateful for everything Bruce gave to search, to this industry, and to the people inside it.

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Inspired by this post on Search Engine Land.

July 3, 2026
My 120-Minute Weekly SEO Workflow That Drives Results

When one person is responsible for paid campaigns, landing pages, reporting, email, social posts, sales requests, and last-minute website updates, I know exactly what usually happens to SEO: it waits.

I have seen this play out on small marketing teams over and over. Everyone knows SEO can bring in qualified demand, reduce dependence on paid media, and support buyers long before they fill out a form. The problem is that SEO rarely feels urgent until traffic drops, rankings slide, or something breaks.

That is why I like a simple 120-minute weekly SEO workflow. It gives me a practical way to protect visibility, find opportunities, improve high-value pages, and turn search data into business impact without pretending I have unlimited time.

Why I keep SEO simple on lean teams

When SEO falls behind, I rarely see effort as the real problem. The bigger issue is usually competing priorities and a lack of clear prioritization.

On a lean team, SEO is one tab among 20. The person responsible for organic growth may also be sending newsletters, briefing designers, updating landing pages, and pulling the report leadership wants by Friday.

Then the advice starts piling up: fix technical issues, publish more, build topical authority, refresh old posts, add schema, improve Core Web Vitals, build links, optimize for AI search, and keep going. Most of that advice may be valid, but no small team can do all of it in one week.

The question I come back to is not, “What could I do?” It is, “What is the highest-leverage thing I can actually finish this week?”

I also try to avoid the reporting trap. It is easy to spend an entire SEO block looking at rankings, traffic, impressions, clicks, CTR, conversions, competitor movement, and keyword shifts. Then the hour ends and nothing ships.

For a small team, reporting has to be short enough to leave room for action. The goal is to decide what to fix next, not to build another dashboard.

Why 120 minutes can be enough

I do not try to run a lean team like an enterprise SEO department. If I audit everything, track everything, collect endless keywords, and ship nothing, I have not improved organic growth.

The point of time-boxing is to force a decision. Every weekly session should end with one or two changes that improve visibility, traffic quality, or conversion potential.

In my 120-minute workflow, I focus on four outcomes: finding what is already working, fixing what is blocking performance, improving the pages closest to revenue, and turning search data into next week’s actions.

I am not trying to “do SEO” for two hours. I am using two focused hours to make decisions and ship work that has a realistic chance of moving the business forward.

My 120-minute weekly SEO workflow

0-15 minutes: Check organic data

I start with a pulse check so I can catch problems before they turn into bigger performance drops.

I look at Google Search Console clicks, impressions, CTR, and average position. I also check organic conversions or assisted conversions in GA4, top landing pages gaining or losing traffic, branded versus non-branded movement, and any indexing, crawling, or manual action warnings.

What I do not do is turn this into a full reporting session. This is not a board deck. I only want to answer one question: is organic visibility moving in a direction that needs action?

My output is a short weekly note: the biggest organic win, the biggest organic concern, one page or query to investigate, and one action to take this week.

15-35 minutes: Find query opportunities

Next, I look for the easiest opportunities in Google Search Console. The richest ones are often queries ranking in positions 4-15 with real impressions. Those pages are already close, and a focused improvement can help them move.

I also watch for pages with strong impressions but weak CTR, queries climbing week over week, and rankings where the current page only partially matches search intent.

I resist the urge to build a long keyword list. Instead, I pick three things: one page to improve, one query to answer better, and one title or meta description to test.

For example, when I reviewed search data for a local accounting client, several queries kept appearing around tax help for freelancers, small-business tax mistakes, and the difference between an accountant and a bookkeeper.

The obvious reaction would have been to write three new articles. Instead, I rewrote one service page around freelancers, added a short FAQ based on those queries, and linked it to an existing bookkeeping article. One page served three search intents, which was far more useful than three unfinished drafts.

35-60 minutes: Improve one money page

This is the most important part of the workflow. I define a money page as any page close to revenue, pipeline, bookings, sales, demos, or consultations.

Money pages can include product pages, service pages, category pages, comparison pages, demo pages, consultation pages, pricing pages, and high-intent landing pages.

My weekly goal is not to optimize the entire website. It is to improve one important page in one meaningful way.

I ask what the buyer needs to believe before converting, what objection is missing, what proof would reduce hesitation, what comparison the buyer already has in mind, and what query the page almost satisfies but does not fully answer.

A meaningful update might be adding three FAQs based on real queries, improving the H1 and introduction, adding comparison language, including proof points, linking to a case study, clarifying who the offer is for, improving the CTA, or adding a short “how it works” section.

That is SEO work, but it is also conversion work. The best page improvements usually help both search engines and buyers understand the value faster.

60-80 minutes: Fix one technical or indexing issue

Technical SEO can take over the full two hours if I let it, so I stay focused on impact.

The question I ask is simple: what could stop an important page from being discovered, understood, indexed, or trusted?

That usually points me toward issues like priority pages not being indexed, broken internal links, redirect chains, duplicate or missing titles on key pages, incorrect canonicals, schema errors on important templates, or valuable pages buried too deep in the site.

I want one of three outcomes from this block: a fix shipped, an issue assigned, or a clear developer brief.

For example, if I find that ecommerce collection pages are not indexed because of incorrect canonical tags, documenting the affected URLs and writing a clear developer brief may be more valuable than publishing another generic article.

80-100 minutes: Improve internal links

Internal linking is one of the fastest SEO wins I can create because it does not require new content.

It helps search engines understand which pages matter, helps users continue their journey, and helps informational content support commercial outcomes.

Each week, I look for links from high-traffic articles to money pages, links from product or service pages to supporting guides, links from older articles to newer strategic content, and opportunities to use clearer anchor text.

If an article ranks for “how to choose accounting software,” I do not want it to be a dead end. I want it to guide readers toward a comparison guide, a relevant case study, and a demo or pricing page. The traffic is already there, so I try to make it more useful.

100-115 minutes: Turn one search insight into messaging

I do not want search data to stay trapped in an SEO silo. The best query I find each week is often a useful signal for the rest of marketing because it shows the language buyers actually use.

A query like “best CRM for small agencies” can become a comparison section on a landing page, a LinkedIn post, a sales email angle, and a paid search ad group.

A query like “is [product] worth it” can become a proof section, a pricing explainer, a “who this is not for” paragraph, or a ready-made answer to a sales objection.

When I share one search insight each week, SEO becomes more than a channel. It becomes a source of customer intelligence.

115-120 minutes: Choose next week’s priority

I end with a decision, not a long list. I choose one clear priority for next week based on business impact, search demand, ease of execution, current performance gap, and proximity to revenue.

The template I use is: “Next week, my highest-leverage SEO action is [X] because [Y].”

For example: “Next week, my highest-leverage SEO action is updating the pricing page because it gets non-branded traffic, supports demo requests, and does not answer implementation cost questions.”

That is how I make SEO operational. The work becomes specific, owned, and easier to repeat.

A sample month for the workflow

To keep the workflow balanced, I like rotating the emphasis each week.

In week one, I focus on a revenue page. I update copy, add FAQs, improve internal links, check indexing and schema, and sharpen the CTA.

In week two, I refresh existing content. I choose one article with impressions but weak clicks or rankings, improve the title, add missing sections, update examples, link to money pages, and better match search intent.

In week three, I handle technical cleanup. I focus on one crawl, indexing, or template issue, such as broken links, duplicate titles, sitemap problems, or a developer brief for a higher-impact fix.

In week four, I turn SEO data into broader marketing assets. That may mean one landing page insight, one sales objection, one content brief, one paid or social angle, or one FAQ or comparison section.

This rotation keeps me from spending every week in dashboards, technical audits, or new content production while ignoring the pages that already have potential.

What I stop doing

Most small teams do not have a doing problem. They have a stopping problem.

I stop chasing every low-impact technical warning. I stop creating content just because a tool found a keyword. I stop publishing AI-assisted articles at scale without a strategy. I stop rewriting pages without a hypothesis. I stop optimizing low-value pages before revenue pages. And I stop treating rankings as the only score that matters.

Before I create new content, I review the pages I already have. The highest returns often come from pages that already rank on Page 2, already get impressions, sit close to revenue, and are one focused update away from doing more.

My test for any task is simple: if I cannot connect it to qualified traffic, conversions, discoverability, buyer education, or trust, it does not belong in the 120 minutes.

How I make it work without a dedicated SEO person

This workflow does not require a full SEO department. It requires one owner, a weekly rhythm, and a bias toward shipping.

A marketing manager can own prioritization and the weekly SEO note. A content marketer can update copy, FAQs, and page sections. A developer or web support partner can handle technical fixes. A paid search manager can share query and conversion insights. A founder or sales team can contribute objections and buyer language.

The owner matters most. Someone has to protect the 120 minutes, choose the priority, and make sure the session ends with an action.

Without ownership, SEO becomes everyone’s job and nobody’s job.

How I use AI to save time

I use AI to shorten repetitive SEO work, not to hand over strategy.

That might mean using a focused workflow to identify queries in positions 4-15, pages with high impressions and low CTR, search queries that should become FAQs, internal linking opportunities, or technical issues that should become developer briefs.

For agencies, client-specific assistants can reduce context switching by remembering each client’s services, priority pages, competitors, and customer objections.

The most useful AI workflows are narrow: a GSC opportunity analyzer, a money page refresh assistant, an internal linking assistant, a technical SEO brief generator, or an SEO reporting summarizer.

I do not want one generic SEO assistant trying to do everything. I want small workflows that help me move faster from data to decisions.

Consistency is the advantage

Small teams win SEO by doing the highest-leverage things repeatedly.

A 120-minute weekly SEO workflow will not replace a full strategy. It will not solve every technical issue, build every content asset, or uncover every opportunity.

But it gives me a practical way to protect visibility, learn from search data, improve revenue pages, and keep organic growth moving.

The mindset is simple: less auditing, more shipping, more buyer intent, less busywork, and more business impact.

Inspired by this post on Search Engine Land.

July 3, 2026
6 Claude Content Audit Workflows I Reuse for Better SEO
I see existing content as a goldmine, but only when I have a practical way to improve it. The hard part is usually finding the time, and that is where Claude has made a large, messy job feel much more manageable for me.

I do not start by building a giant content audit system. I start with one article, run one focused audit, refine the output, and then turn the prompt into a reusable Claude skill. Over time, those one-off audits become a working library I can improve every time I use it.

I use Claude to uncover topical gaps, flag outdated information, check brand voice, and evaluate whether a page is easy for AI systems to retrieve and cite. The real value comes from iteration: each time I improve a skill, the next audit becomes faster and more useful.

Here are six content audit workflows I would build in Claude. The first four work at the page level, so I can start with a single article before moving into larger library-wide analysis.

Page-level audits

When I am not ready to build a full workflow, I start with page-level audits. These audits only require one article, which means I do not need a content inventory, a data export, or a complicated setup. After each session, I ask Claude to turn the process into a reusable skill for future page-level reviews.

1. Brand voice consistency

I use a brand voice consistency audit when a content library has drifted over time. Voice can shift because of new writers, changing services, product updates, or evolving positioning. This audit helps me spot where a page no longer sounds aligned with the brand.

If I do not have detailed brand guidelines with strong examples, I let Claude extract the voice guide from high-quality content. That usually works better than relying on vague phrases like “conversational but authoritative” or “educational, not too formal.”

I pick three to five articles that represent the brand at its best. If possible, I download them as markdown files and ask Claude to describe how the voice works in concrete terms.
- How the articles usually open, such as whether they begin with a direct claim, a counterintuitive statement, or a specific scenario.
- How sentences and paragraphs are built, including average length, range, rhythm, and how paragraphs tend to close.
- Three to five personality dimensions framed as “We say X, but not Y,” with do and don’t examples.
- Words and phrases the brand tends to use, and words or phrases it should avoid.
- Specific constructions, phrases, and conventions the brand never uses.
Instead of accepting a vague voice description, I want Claude to return concrete observations. For example, it might say that articles open with a direct claim rather than a scene-setting paragraph, sentences average 15 to 20 words and rarely exceed 30, and transitions are functional, such as “here’s why that matters,” rather than formulaic, such as “furthermore.”

I also want example pairs, such as: “We’d say ‘the data shows three things,’ not ‘there are multiple factors to consider.’” The goal is not to create a voice guide for writers. The goal is to create one an LLM can understand and apply consistently.

Once I like the output, I ask Claude to save it as a skill and evaluate an article against it. If Claude flags issues I disagree with, I update the skill until the feedback becomes useful and repeatable.

I can then use that skill to find voice inconsistencies in older content, check new drafts for alignment, and even generate more on-brand first drafts. I still edit the output, but the starting point is much stronger.

Dig deeper: How to train Claude to sound like your brand

2. Coverage comparison

When I need to improve content performance, I use a coverage comparison to find topical gaps. This helps me understand what competing pages cover that my article misses.

I use the Claude in Chrome extension to have Claude review the top three to five ranking pages for my target keyword. Then I ask Claude to compare those pages against my content and highlight the most important gaps.
- What competitors are doing well.
- What my article already does well.
- Where I can improve the piece without bloating it.
If I want the output in a table, I ask Claude to format it that way. If I want a downloadable DOCX for review or handoff, I ask for that instead.

When Claude recommends additions I would never publish, I make a note of those exclusions before packaging the workflow into a skill. That way, the skill gets closer to my editorial standards each time I refine it.

3. Freshness audit

Old content adds up quickly, and it is hard to prioritize refreshes while I am also producing new material. A freshness audit skill helps me identify what needs attention without rereading every older article from scratch.

I give Claude an older article and ask it to flag anything time-sensitive: statistics tied to a specific year, named tools or platforms, references to “current” or “recent” trends, and claims that depend on a market, regulatory, or product context that may have changed. I am not asking Claude to rewrite the article yet. I am asking it to build an issue list I can act on.

If my company has launched new products, removed old services, changed positioning, or updated terminology, I include that context in the input. That helps Claude flag what should be added, removed, or revised.

Dig deeper: How to turn Claude Code into your SEO command center

4. AEO and AI retrievability

I use an AEO and AI retrievability audit to understand whether a page is likely to be surfaced in AI-generated answers. Tools such as ChatGPT, Perplexity, and Google AI Overviews tend to favor content that answers questions directly. If an article buries the answer under too much preamble, or structures key information in a way that is hard to extract, it becomes less useful for those systems.

I give Claude the article and the target query, then ask it to evaluate several retrieval signals.
- Whether the article answers the main question directly and early.
- Whether key statements are specific enough for an LLM to quote or cite.
- Where an FAQ-style section would improve clarity.
- Whether the page includes authority signals, such as primary research, first-person experience, outbound citations, or specific examples.
Once I save this as a skill, it becomes an extra editor focused specifically on AI visibility and answer retrieval.

Library-level audits

Once I am ready to move beyond individual pages, I use library-level audits. These require performance data, a content inventory, a connector, or a manual export.

5. Performance triage

When I think about a traditional content audit, performance triage is usually what comes to mind. It helps me analyze a content library and identify the pages that deserve attention first.

Before I begin, I make sure Claude has access to the right data through a connector such as BigQuery or the Semrush API. If that is not available, I export the data I normally use for large-scale audits, such as traffic, clicks, engagement metrics, conversions, rankings, and related performance signals.

I ask Claude to prioritize pages that have suffered meaningful performance drops in the past six to 12 months, pages with high impressions but consistently low click-through rates, and pages that have been live long enough to rank but never gained traction.

I also define what a meaningful performance drop looks like for the site I am analyzing, because traffic patterns vary by industry, audience, and page type. Then I ask Claude for a prioritized list of what is worth investigating and why. From there, I use the page-level audits above to diagnose the problem.

If I have run this analysis before, I give Claude the previous output. That helps the skill learn the kind of prioritization and reasoning I expect.

Dig deeper: How to build a Claude Code-powered second brain for agency work

6. Topical gap analysis

I treat entities as a major part of AEO and semantic search. A topical gap analysis helps me see whether my content library has enough coverage to build authority around the entities tied to my brand.

The core question I ask is simple: what is my content library not covering that it should?

To start, I create a list of target entities. For example, at my agency, I want to be known for SEO and AEO. If I have a clear list of services or products, I can use that instead of a formal entity list.

Using Cowork or Code, I ask Claude to analyze my sitemap and compare it to those target entities. If I have a Screaming Frog export with URLs, page titles, and meta descriptions, I use that as input for a more accurate analysis.

Then I ask Claude to identify topic clusters that are missing or underrepresented based on the target entities, services, or products. If I want prioritization, I can use the Semrush MCP so Claude can check search volume for potential keywords.

Not every gap is worth filling. I filter the results against audience needs, business relevance, and editorial standards. Then I feed those decisions back into Claude so the skill produces better recommendations next time. The final list can go directly into my content creation workflow or be handed off to a content team.

I do not try to audit everything at once

I have seen content audits stall because the scope feels too large, not because the team lacks data. My preferred approach is to pick one audit and one article, run the workflow, save the skill, and use it again on the next piece.

For me, iteration is part of the value. I enjoy taking one Claude skill, improving it, and then chaining it with other skills to uncover more content opportunities. Starting small is what makes the system easier to keep using.

Inspired by this post on Search Engine Land.
July 3, 2026
Does llms.txt Matter for AI SEO? What My Data Shows
I have watched the debate around llms.txt become one of the most polarized conversations in web optimization.

Some people treat llms.txt as essential infrastructure for AI discovery. Others, especially longtime SEO practitioners, see it as speculative theater. Platform tools are starting to flag missing llms.txt files as site issues, yet server logs still show that AI crawlers rarely request them.

Google even appeared to adopt it. Sort of. In December, Google added llms.txt files across many developer and documentation sites.

At first, the signal looked obvious to me: if the company behind the sitemap standard was implementing llms.txt, maybe the file really mattered.

Then Google removed it from its Search developer docs within 24 hours.

Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.

The llms.txt research

I wanted data, not another debate.

So I tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care. I looked at the 90 days before implementation and the 90 days after.

I measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and the other changes each site made during the same window.

Here is what I found:
- Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt was not the cause.
- Eight sites saw no measurable change.
- One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file

The Neobank: 25% growth

One digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, its AI traffic was up 25%.

That sounds compelling until I looked at what else happened during the same period.
- The company ran a PR campaign around its banking license and earned coverage in major national publications.
- It restructured product pages with extractable comparison tables for interest rates, fees, and minimums.
- It published 12 new FAQ pages optimized for extraction.
- It rebuilt its resource center with new banking information and concepts.
- It fixed technical SEO issues, including header structure problems.
When a company earns Bloomberg coverage in the same month it launches optimized content and fixes crawl errors, I cannot isolate llms.txt as the growth driver.

The B2B SaaS platform: 12.5% growth

A workflow automation company saw AI traffic jump 12.5% two weeks after implementing llms.txt.

The timing looked perfect. It would be easy to call the case closed. But the surrounding context told a different story.

Three weeks earlier, the company had published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. These were functional tools, not ordinary content marketing assets, and they drove the engagement behind the spike.

Google organic traffic to those templates rose 18% during the same period and kept climbing throughout the 90 days I measured.

Search engines and AI models surfaced the templates because they solved real problems and created an entirely new site section. They did not surface them simply because the URLs appeared in an llms.txt file.

The 8 sites where nothing happened after uploading llms.txt

Eight sites saw no measurable change after adding llms.txt. One of them declined by 19.7%.

The decline came from an insurance site that implemented llms.txt in early September. Based on the data, the drop likely had nothing to do with the file.

The same pattern appeared across all traffic channels. Llms.txt did not prevent the decline, and it did not create any visible advantage.

The other seven sites, which included ecommerce brands in pet supplies, home goods, and fashion, plus B2B SaaS, finance, and pet care sites, used llms.txt to document their best existing content. That content included product pages, case studies, API docs, and buying guides.

Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file did not change that.

The pattern was clear: sites that launched new, functional content saw gains. Sites that only documented existing content saw no gains.

Why the disconnect?

No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.

Google’s Mueller put it plainly:
- “None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That is the reality I saw in the data. The file exists. The advocacy exists. But platform adoption does not show meaningful use yet.

The token efficiency argument and its limits

The strongest case for llms.txt is efficiency. Markdown can save time and tokens when AI agents parse documentation. It gives agents clean structure instead of forcing them through complex HTML, navigation, ads, and JavaScript.

Vercel says 10% of its signups come from ChatGPT. Its llms.txt includes contextual API descriptions that help agents decide what to fetch.

That matters, but mostly for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency can improve integration.

For ecommerce brands selling pet supplies, insurance companies explaining coverage, or B2B SaaS companies targeting nontechnical buyers, token efficiency does not automatically translate into traffic.

llms.txt is a sitemap, not a strategy

The closest comparison I can make is a sitemap.

Sitemaps are useful infrastructure. They help search engines discover and index content more efficiently. But I would not credit traffic growth to simply adding a sitemap. The sitemap documents what exists; the content drives discovery.

Llms.txt works in a similar way. It may help AI models parse a site more efficiently if they choose to use it, but it does not make the content more useful, authoritative, or likely to answer user queries.

In my analysis, the sites that grew did so because they:
- Created functional assets such as downloadable templates, comparison tables, and structured data.
- Earned external visibility through press and backlinks.
- Fixed technical barriers such as crawl and indexing issues.
- Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It did not drive them.

What actually works

The two successful sites showed me what actually matters.
- Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced them because they solved real problems, not because they appeared in a markdown file.
- Structure content for extraction. The neobank rebuilt product pages with comparison tables for interest rates, fees, and account minimums. That is data AI models can pull directly into answers without heavy interpretation.
- Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models cannot access your content, no amount of documentation will help.
- Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assessed authority.
- Optimize for user intent. Both sites answered specific queries, such as “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users ask, not content that is merely well documented.
None of this requires llms.txt. All of it can drive results.

Should you implement an llms.txt file?

If you run a developer tool and AI coding assistants are a primary distribution channel, I would implement llms.txt. In that context, token efficiency matters because your audience is already using agents to work with documentation.

For everyone else, I would treat llms.txt like a sitemap: useful infrastructure, not a growth lever.

It is good practice to have. It likely will not hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.

Those tactics have shown real ROI in AI discovery. Llms.txt has not, at least not yet.

The lesson I take from this is not that llms.txt is bad. It is that we are reaching for control in a system where the rules are still being written. Llms.txt offers comfort because it is concrete, actionable, and familiar. It looks like the web standards we already understand.

But looking like infrastructure is not the same as functioning like infrastructure.

My focus would stay on what is already working:
- Create useful content.
- Structure it for extraction.
- Make it technically accessible.
- Earn external validation.
Platforms and formats will change. The fundamentals will not.

Inspired by this post on Search Engine Land.
July 3, 2026
Organize Your Profound Space with Folders and Favorites

I’m excited to share that you and I can now easily sort our Agents and Sheets in Profound. The new feature allows us to organize them into folders, sub-folders, and even mark them as favorites for quick access.

Imagine the convenience of having all your important files just a click away, neatly categorized and prioritized as per your needs. This enhancement is designed to save us time and boost our productivity, making our workflow smoother and more efficient.

Inspired by this post on Try Profound Blog.

June 19, 2026
Harnessing Psychology: Create Persuasive Content That Converts

I’ve noticed that TikTok Shop creators excel by tapping into the psychology that drives people to act. Let me share how we can leverage these persuasive principles in our writing.

SEO content is often designed to rank, but conversion can sometimes fall by the wayside when we’re caught up in the technical checklist. In light of AI Overviews and falling click-through rates making visibility more challenging, I believe it’s time to focus on whether our content encourages action once someone engages with it.

Take a cue from TikTok Shop creators—they don’t just thrive because of large followings. They master persuasion by understanding consumer psychology and scaling actions. This insight can transform how we approach our written content.

The formula that successful TikTok Shop creators follow isn’t random. It relies on consumer psychology principles, not on celebrity status or follower count. I’ve realized that 99% of my own video views come from non-followers. Therefore, it’s the understanding of the psychology behind actions that matters.

By focusing on visual hooks, psychological triggers, storytelling, and relentless experimentation, we can apply these elements to written content to drive similar results.

People often buy based on emotions, justifying their decisions rationally later. It’s crucial to connect with their motivations rather than just presenting facts.

Persuasive content succeeds because it targets human desires like protecting loved ones, enjoying life, feeling safe, and seeking social approval.

Understanding these motivations allows me to craft content that resonates more deeply with my audience, ultimately leading to better engagement and conversion rates.

Inspired by this post on Search Engine Land.

May 28, 2026
Unlock the Hidden SEO Strategy in Buyer Journeys
I realized that most content tends to meet users right where they are. When someone looks up “best MBA programs,” they typically get a list of MBA programs. But I’ve discovered that sometimes the most valuable content can challenge the very assumptions behind these queries. It’s about offering alternatives that users never knew they should explore.

Taking the initiative to broaden user awareness beyond their typical path often gets overlooked in SEO and content marketing strategies. However, when done thoughtfully, it helps position my products and services to rank for a wider array of keywords while enlightening my audience about various solutions to their issues.

Imagine someone searching for a certain degree, medication, certification, or product. They often seem to have settled on a solution without fully evaluating their problem. By crafting content that gently introduces alternatives like “apprenticeships vs. four-year degrees” or “herbal supplements vs. prescription options,” I find I can attract high-intent traffic and offer more value than just matching the initial intent.

Allow me to share a roadmap on integrating this strategy into ongoing editorial processes.

LLMs are already doing this

I’ve noticed how LLMs and AI Overviews already employ a version of this strategy. After addressing a query, they often probe further, asking if you wish to delve deeper into the topic or learn about alternatives. Following this path with an LLM can guide users toward opportunities they hadn’t considered.

For example, I was searching for mood and stress supplements. While LLMs and AI are not replacements for medical advice (always consult with a healthcare provider before altering diet or supplements), they offered some intriguing suggestions. By entering what I was already taking into ChatGPT, it not only provided feedback but also posed additional questions, enhancing the discussion.

Through our back-and-forth, the AI went beyond general advice, offering modifications I hadn’t thought to ask about, integrating details like my caffeine habits into its suggestions.

This approach allows me to guide audiences towards solutions they might not have initially considered.

How to Identify Beneficial Queries

When optimizing for “mood and stress supplements,” I try to think beyond the obvious. Many might be searching for such products because they feel overwhelmed. They may be seeking ways to cope during a stressful period. From there, I can extend my keyword research to discover topics about stress relief and produce content that presents additional methods for stress management.

Conversely, a user might begin their quest believing meditation or nature walks are the solutions for their stress and mood improvement. Yet, they might be unaware of mood supplements. So, while it’s wise for a supplement company to cultivate content regarding mood and stress products, it’s also prudent to explore other solutions for user problems.

Embedding product suggestions within broader articles about sleep and stress can introduce readers to options they hadn’t initially thought about.

Structuring Content Around Alternative Solutions

Quality and value are what I prioritize when crafting this kind of content. When users encounter valuable information, they tend to stay engaged longer, explore related links, and perceive my content as a reliable resource.

The goal is to rank for the primary intent while skillfully introducing my unique solutions. Beyond text, other ways to guide users include:
- Free templates or tools, even alongside paid offerings.
- User stories that depict varied experiences.
- Educational events like webinars or workshops tying into my offerings.
The key is to ensure product mentions feel natural rather than forced into promotional content. When done subtly, such mentions can shift user perceptions and expand their problem-solving landscape.

Keyword and SERP Signals that Signify Openness

I’ve come to recognize when users might be open to journey-interrupting options by identifying keywords suggesting they’re still in the research phase versus ready to make a purchase.

Branded Terms

Someone searching [“brand name” buy] is usually more intent on purchasing compared to those exploring [“brand name” reviews] or [“brand name” competitors], which signal ongoing research.

Industry ‘Widetail’ Queries

I coined the term “widetail” queries to cover a broad array of searches that fall within the same user journey. For instance, a user needing their lawn mowed might search numerous related topics, each a piece of the broader issue.
- “Robot lawnmower price”
- “Lawn service near me”
- “How often to cut grass?”
By thinking beyond straightforward service offerings and tapping into these peripheral queries, I capture more of those in the early stages of their journey.

When Ethical Guardrails Are Needed

While discussing supplements, it’s crucial to approach this strategy responsibly. Especially in areas like healthcare, careers, or finance, it’s my duty to ensure content doesn’t falsely position a product as a solution to serious issues. FDA and FTC guidelines are there to protect users from misleading claims and to ensure safety.

Interrupting Buyer Journeys at the Right Time

Consider the lawn care example again; multiple funnels can direct toward the goal of alleviating lawn maintenance burdens. Each query is a part of the user’s overarching journey. By broadening the scope of content, I appear not just during basic comparison searches but also amidst tangential research paths.

Strategically expanding content helps catch the attention of those not expecting it, increasing search traffic, leads, and creating a loyal audience pleased to discover my brand.

Inspired by this post on Search Engine Land.
May 26, 2026
Unlock Coding Potential with the Profound API Cookbook

Hey there! I’m excited to introduce you to something that has truly changed the way I approach coding projects—the Profound API Cookbook. If you’ve ever started with the thought, ‘I want this number,’ and wished for a seamless way to transform that into runnable code, this is for you.

Imagine having a collection of end-to-end recipes right at your fingertips, perfectly layered on top of our REST API references. This isn’t just about coding; it’s about enhancing your workflow and efficiency in a whole new way. Each recipe is designed to guide you from concept to execution with ease.

Inspired by this post on Try Profound Blog.

May 20, 2026
Explore Google’s New AI Search Link & Citation Updates

Have you noticed a change in how Google displays links and citations in its AI search features? I recently learned about five key updates that aim to enhance our experience with AI Mode and AI Overviews.

According to Hema Budaraju, VP, Product Management at Google, these upgrades are designed to help us connect with authentic voices and access valuable information across the web. She detailed these updates in a recent article.

Let’s dive into the updates rolling out:

(1) Suggested angles at the end of AI responses. Google now suggests further reading options at the end of AI responses. These link to unique articles or analyses that deepen our understanding of the topic. It’s like having a roadmap to satisfy our curiosity!

Here’s a preview of this feature:

(2) Easier access to your news subscriptions. With this update, Google displays links from our news subscriptions prominently. This means I can quickly access content I trust, maximizing the value of my subscriptions. During Google’s early tests, these subscription links significantly boosted click-through rates.

If you’re a publisher, check out the documentation to enable this feature.

Here’s what this looks like in action:

(3) Social media and online discussions now include creator details. When AI features cite social media, Google includes not only the website’s name but also the creator’s name, handle, and community name. This transparency helps me spot firsthand sources at a glance.

Here’s a glimpse of how this plays out:

(4) More links, next to relevant text. Google is increasing the number of links shown directly within AI responses, strategically placing them next to relevant text. This makes it tempting for me to explore these sources further.

Here’s what it looks like:

(5) Hover over inline links for a quick look. Now when I hover over an inline link in Google’s AI features, I get a sneak peek of the website. This could just be the nudge I need to click through and explore further. I remember seeing Google test this back in February and thought it was a brilliant idea.

Here’s an example of the feature:

Why this matters. Google is committed to ongoing testing and refinements, ensuring these features serve us better. I truly believe these changes will promote more engagement with the cited pages, presenting an exciting step forward for both users and the web ecosystem. The real question is, will they meet my expectations?

Inspired by this post on Search Engine Land.

May 6, 2026