This article is the practical companion to the DRAFT framework article, which introduced a five-stage workflow for writing AI-assisted, search-optimised content without losing your voice. While the first article covered the process, this one covers the specific elements you should optimise, and explains what each one does, and why it matters for getting your content in front of the right people.

None of this requires you to be an SEO specialist. If you can structure a scientific paper, you can structure a blog article for search. The principles are closer than you think.

Subscribe now

Headings: your article’s architecture

Headings are not decoration. They are the structural skeleton of your article — and search engines read them that way.

Your H1 is your article title. You get one per page, and it should clearly state what the article is about. Below that, H2 headings mark your major sections, and H3 headings mark subsections within them. This nesting needs to be logical: an H3 should always sit within an H2, never float independently.

Why this matters: search engine crawlers use heading hierarchy to understand the thematic structure of your content [3]. A well-structured heading tree tells Google what your article covers, how the ideas relate to each other, and which concepts are most important. It also helps readers — particularly busy professionals scanning for the section most relevant to them.

For researchers and technical writers, think of it this way: your H1 is your paper title, your H2s are your major section headings (Introduction, Methods, Results, Discussion), and your H3s are your subsections within each. You already know how to do this. The only difference is that on the web, these headings carry SEO weight — they signal topic relevance to search algorithms, not just to human readers.

A few practical guidelines: keep headings descriptive rather than clever (save the wordplay for social media), include terms your audience would actually search for, and make sure each heading accurately reflects the content beneath it. A heading that promises something the section doesn’t deliver is worse than a bland one.

Pro tip: Read your headings in sequence, ignoring the body text. Do they tell a coherent story? Could someone scanning just the headings understand what your article covers and in what order? If not, your structure needs work — and that’s a problem for both SEO and readability.

Meta title and description: your abstract for search

When your article appears in search results (i.e. SERP = search engine result page), two things determine whether someone clicks: the meta title and the meta description. Together, they function like a miniature abstract — a compressed summary that needs to communicate value in very limited space.

Example of results displayed on the search engine result page of Google.

The meta title appears as the blue clickable link in search results. Google typically displays around 60 characters before truncating, so every word needs to earn its place. It doesn’t have to match your H1 exactly — in fact, a slightly shorter, keyword-focused version often works better for search while your H1 can be more expansive on the page itself.

The meta description is the two-line summary beneath the title. You have roughly 155 characters to convey what the article covers and why it’s worth reading. Google doesn’t always use the description you provide — it sometimes generates its own based on page content — but providing a well-crafted one increases the chances your intended message is what users see [4].

Why this matters: meta information doesn’t directly affect ranking position, but it significantly affects click-through rate — the percentage of people who see your listing and actually click on it [5]. A compelling meta description can be the difference between your article being found and being scrolled past. For expert content competing against lower-quality but more numerous AI-generated pages, a precise, authoritative meta description signals quality before the reader even arrives at your site.

For researchers accustomed to writing abstracts: the skill is the same, the constraint is tighter. An abstract might give you 300 words. A meta description gives you roughly 25. Distil the core value of your article into a single statement that answers: what is this about, and why should I read it?

Pro tip: Write three to five versions of your meta description and pick the best one. Or ask AI to generate options — this is exactly the kind of task where AI excels. Just make sure the version you choose is accurate and sounds like something you’d actually write. If it reads like a generic marketing pitch, rewrite it.

Internal linking: building your body of work

Internal links are the hyperlinks that connect one page on your site to another. When you mention a concept you’ve covered in a previous article and link to it, that’s an internal link. It seems simple, but it’s one of the most underused SEO tools — especially among experts who tend to treat each article as a standalone piece.

Why this matters: internal links serve two distinct purposes. For readers, they provide pathways to deeper or related information — the same function as cross-references in academic work. For search engines, they create a map of your content, showing how your articles relate to each other and which topics you cover in depth [6]. This is how search engines assess topical authority — the signal that you’re not just writing a single article on a subject but building a sustained, interconnected body of knowledge.

Think of it as the digital equivalent of your publication record. A single paper on a topic shows interest. A series of papers, each referencing and building on the others, demonstrates expertise. Internal linking creates that same signal for search engines.

The practical approach: every new article should link to at least two or three related pieces on your site. And when you publish something new, go back to older related articles and add a link to the new one. This bidirectional linking strengthens the connections and keeps your older content relevant.

Be intentional about anchor text — the clickable words in the link. “Click here” tells search engines nothing. “Our guide to the DRAFT framework for AI-assisted blog optimisation” tells them exactly what the linked page covers and reinforces topic relevance.

Pro tip: Keep a simple spreadsheet of your published articles, their main topics, and their URLs. When you write a new piece, scan the list for natural linking opportunities. This takes five minutes and prevents the common problem of older content becoming isolated from your growing body of work.

External links: citing your sources

External links point from your article to other websites — and they function much like citations in academic writing. When you link to an authoritative source that supports a claim, you’re doing two things: giving the reader access to the evidence, and signalling to search engines that your content is grounded in credible information.

Why this matters: Google’s systems evaluate trustworthiness partly by looking at the sources a page references [3]. Linking to primary sources — peer-reviewed research, official documentation, institutional reports — reinforces the credibility of your own content. It signals that your arguments are evidence-based, not opinion dressed as fact.

For researchers, this is second nature. You cite your sources. The web equivalent is linking to them. The key difference is that on the web, your links should point to the source itself — the journal article, the official report, the institutional page — rather than secondary summaries or aggregator sites where possible.

A few things to avoid: don’t link to low-quality or unrelated pages just to have external links. Don’t link to competitors’ content unless it genuinely adds value for your reader. And always check that your links actually work — broken links are a minor but real negative signal, and they frustrate readers.

Pro tip: When referencing a statistic or claim, link to the primary source rather than the news article that reported it. This is basic academic practice, but it’s surprisingly uncommon online — and search engines recognise the difference between content that cites originals and content that cites summaries.

Alt text on images: describing what you show

Alt text (alternative text) is a short written description attached to an image in your article’s HTML. It was originally designed for accessibility — screen readers use it to describe images to visually impaired users — and it remains one of the most fundamental requirements in the WCAG standard. Success Criterion 1.1.1 (Non-text Content) states that all non-text content must have a text alternative that serves the equivalent purpose [1]. It’s a Level A criterion — the most basic level of conformance — meaning it’s considered the bare minimum for an accessible web.

But alt text also serves an SEO function: it tells search engines what your image depicts, since crawlers can’t “see” images the way humans do.

Why this matters: if your article includes figures, diagrams, data visualisations, or photographs, alt text is how search engines index that visual content [7]. Without it, your images are invisible to search. With well-written alt text, they become an additional discovery pathway — someone searching for a specific type of diagram or chart could find your article through image search.

For researchers and technical authors, this is particularly relevant. Your articles are likely to contain figures, graphs, and process diagrams that carry significant informational value. Good alt text captures that value in words.

The approach is straightforward: describe what the image shows and why it matters in the context of the article. “Bar chart comparing publication rates across three funding models, showing a 40% increase under Model C” is useful alt text. “Chart” is not. “Image” is useless.

Keep alt text concise — one or two sentences is usually sufficient. And avoid keyword stuffing: the purpose is to describe the image accurately, not to cram in search terms. Search engines can detect the difference, and so can screen readers.

Pro tip: If you wouldn’t include the image without a caption in a journal article, don’t include it without alt text on your website. The principle is the same: every figure should be described and contextualised.

Structured data: speaking the language of search engines

Structured data — also known as schema markup — is a layer of code added to your page that explicitly tells search engines what type of content it contains. Rather than leaving Google to infer that your page is a blog article written by a specific author on a specific date, structured data states it directly in a format machines can parse.

Why this matters: structured data helps search engines categorise and display your content more effectively. For blog articles, the most relevant schema types are Article (or BlogPosting) and Person (for author information). When implemented correctly, structured data can influence how your article appears in search results — enabling richer displays that include author attribution, publication date, and article type [8].

Since Google’s March 2026 core update, structured data has taken on additional significance. Google’s AI-powered features now use schema markup as a trust and verification signal when selecting sources for AI-generated answers [9]. Accurate structured data increases the likelihood that your content will be cited in AI overviews — an increasingly important discovery channel.

For most researchers and writers, you won’t need to implement structured data yourself. Modern CMS platforms like WordPress, Squarespace, and Substack handle much of this automatically. But it’s worth understanding what it is and verifying it’s in place — particularly author information (your name, credentials, and a link to your author profile) and article metadata (publication date, date modified, article type).

If you’re using WordPress, plugins like Rank Math can manage structured data without requiring any coding. For other platforms, Google’s Rich Results Test is a free tool that lets you check what structured data a page contains and whether it’s valid [10].

Pro tip: Make sure your author schema includes a link to a dedicated author page or professional profile. Google increasingly connects content to known entities — real, identifiable people with verifiable credentials. For researchers and technical experts, this is your E-E-A-T signal: linking your structured data to a profile that shows your publications, affiliations, and expertise tells search engines exactly why you’re qualified to write on this topic [3].

URL slug: your article’s permanent address

The URL slug is the last segment of your article’s web address — the part that comes after your domain name. For this article, the slug might be /seo-for-expert-content-what-to-optimise. It seems like a small detail, but it contributes to both usability and searchability.

Why this matters: a clean, descriptive URL slug gives both readers and search engines an immediate signal about what the page contains [11]. Compare /seo-for-expert-content-what-to-optimise with /post-2026-04-draft-v3-final. The first communicates topic relevance. The second communicates nothing.

Keep slugs short, descriptive, and hyphen-separated. Include your primary keyword or topic naturally. Remove unnecessary words like “and,” “the,” or “a.” Most CMS platforms generate slugs automatically from your title, but it’s worth checking and editing them before publication — once a URL is live, changing it can create broken links and redirect issues.

Pro tip: Set your URL slug before you publish, not after. Changing a URL after publication means you need to set up a redirect from the old address to the new one — otherwise anyone who bookmarked or linked to the original URL will hit a dead end. Getting it right the first time saves you a headache later.

Putting it all together

None of these elements works in isolation. A well-structured heading hierarchy with a poor meta description still loses clicks. Excellent alt text on images doesn’t help if your headings are vague and your internal links are non-existent.

The real power of on-page SEO is cumulative: each element reinforces the others to create a page that is coherent, discoverable, and trustworthy — both to search engines and to the human beings you’re writing for.

One final element worth mentioning: readability and visual presentation. While not an SEO ranking factor in itself, your content’s visual accessibility affects how long people stay on the page — and dwell time is a signal search engines notice. WCAG 2.2 Success Criterion 1.4.3 (Contrast Minimum) requires a contrast ratio of at least 4.5:1 between text and its background [10]. Light grey text on a white background may look elegant, but it’s difficult to read for anyone with low vision — and frustrating for everyone else. Similarly, WCAG 2.2 Success Criterion 1.4.12 (Text Spacing) ensures that content remains readable when users adjust line height, letter spacing, or word spacing to suit their needs. If your CMS template is well-built, these should be handled by default — but it’s worth checking.

If you’re implementing these for the first time, start with the highest-impact items: headings, meta title and description, and internal links. These require no technical knowledge and deliver the most immediate improvement. Then layer in alt text, structured data verification, and URL slug hygiene as you build the habit.

FAQs

• Do I need to optimise every element for every article? Ideally, yes — but if you’re just starting out, focus on headings, meta title, meta description, and internal links. These four elements cover the most ground with the least effort. Add alt text and structured data verification as you become more comfortable with the process.

• Does my CMS handle any of this automatically? Most modern CMS platforms handle some elements automatically. WordPress, Squarespace, and Substack all generate basic structured data, URL slugs, and heading structures. However, automatically generated content isn’t always optimised — auto-generated meta descriptions tend to be generic, and default URL slugs can be unnecessarily long. It’s worth reviewing and refining what your CMS produces.

• How is on-page SEO different from technical SEO? On-page SEO — what this article covers — focuses on the content and structure of individual pages: headings, metadata, links, images, and text. Technical SEO deals with the infrastructure of your website: site speed, mobile responsiveness, crawlability, security certificates, and server configuration. Both matter, but on-page SEO is where content creators have the most direct control.

• Can I over-optimise my content? Yes. Keyword stuffing — forcing search terms into headings, alt text, and body copy at the expense of readability — can actually harm your ranking. Search engines are sophisticated enough to recognise when content is written for algorithms rather than people [1]. Optimise naturally: use relevant terms where they fit, and prioritise clarity over keyword density.

• Should I hire an SEO specialist? For content-level optimisation — what this article covers — you probably don’t need one. Researchers and technical writers already have the analytical skills to implement these elements effectively. Where an SEO specialist adds value is in technical SEO, competitive analysis, and long-term content strategy at scale. If you’re publishing regularly and want to build sustained visibility, a consultation can be worthwhile.

• How do I know if my optimisation is working? Google Search Console is a free tool that shows you which queries are driving traffic to your articles, your average ranking position, and your click-through rates. Set it up and check it monthly. If you see your articles appearing for relevant queries and your click-through rate is healthy, your optimisation is working. If not, revisit your meta descriptions and headings first — they’re the most common culprits.

• How much do I need to know about web accessibility? You don’t need to become a WCAG expert. But the overlap between good SEO practice and accessible design is significant enough that by following the guidance in this article — proper heading hierarchy, descriptive alt text, meaningful link text — you’re already meeting several WCAG 2.2 criteria. For researchers at public institutions, this is increasingly relevant: accessibility regulations referencing WCAG are now in force or incoming across the EU (European Accessibility Act), the US (ADA Title II), and New Zealand (Web Accessibility Standard) [11]. If your content is well-structured, clearly written, and properly labelled, you’re most of the way there.

References

[1] W3C Web Accessibility Initiative, “Web Content Accessibility Guidelines (WCAG) 2.2,” W3C Recommendation, October 2023. Available: https://www.w3.org/TR/WCAG22/

[2] W3C Web Accessibility Initiative, “WCAG 2 Overview,” W3C. Available: https://www.w3.org/WAI/standards-guidelines/wcag/

[3] Google Search Central, “Creating helpful, reliable, people-first content,” Google Developers. Available: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[4] Google Search Central, “Control your snippets in search results,” Google Developers. Available: https://developers.google.com/search/docs/appearance/snippet

[5] Google Search Central, “SEO Starter Guide,” Google Developers. Available: https://developers.google.com/search/docs/fundamentals/seo-starter-guide

[6] Google Search Central, “Links best practices for Google,” Google Developers. Available: https://developers.google.com/search/docs/crawling-indexing/links-crawlable

[7] Google Search Central, “Google Images best practices,” Google Developers. Available: https://developers.google.com/search/docs/appearance/google-images

[8] Google Search Central, “Article structured data,” Google Developers. Available: https://developers.google.com/search/docs/appearance/structured-data/article

[9] Digital Applied, “Schema Markup After March 2026: Structured Data Update,” March 2026. Available: https://www.digitalapplied.com/blog/schema-markup-after-march-2026-structured-data-strategies

[10] Google Search Central, “Rich Results Test,” Google Developers. Available: https://search.google.com/test/rich-results

[11] Google Search Central, “URL structure,” Google Developers. Available: https://developers.google.com/search/docs/crawling-indexing/url-structure

Go further

1/ Explore how I help scientists build strategic websites → Visit tiffanysiegel-sci.com
2/ Follow along for weekly strategic insights → Connect on LinkedIn

Google has just released an official guide on optimising for generative AI features on Search, covering AI Overviews, AI Mode and the broader generative experience [1]. If you’ve been wondering whether to bin your SEO playbook and pivot to “AEO” or “GEO” hacks, this is the clarifying read you’ve been waiting for.

Here are the five takeaways I think matter most — especially if you’re a researcher, academic, founder or technical expert trying to separate signal from noise.

Subscribe now

#1. GEO and AEO are not separate disciplines — they are SEO

Let’s start with the headline. In Google’s own words: optimising for generative AI search is optimising for the search experience, and thus still SEO [1]. google

That single line settles a lot of debate. AEO (answer engine optimisation) and GEO (generative engine optimisation) have been marketed as new disciplines — sometimes with their own workshops, certifications and agency packages. From Google Search’s perspective, they are not.

The reason is technical, and worth understanding. Google’s generative AI features sit on top of the same ranking and quality systems that already power classic Search [1]. Two mechanisms do the heavy lifting:

• Retrieval-augmented generation (RAG), which pulls relevant pages from the Search index to ground AI responses in real, indexed content [1].

• Query fan-out, which generates a set of related sub-queries to fetch additional results for a single user question [1].

#2. Foundational SEO is still the bedrock

The guide is explicit — the SEO Starter Guide and Search Essentials continue to apply, because the generative features are rooted in the same core systems [1, 2].

In practice, that means the same fundamentals you’ve (hopefully) been investing in:

• A site that is crawlable, indexable and meets Google’s technical requirements.

• A clear technical structure with sensible HTML, no JavaScript blocking issues, and well-handled redirects.

• A good page experience — Core Web Vitals, mobile-friendliness, low latency.

• Reduced duplicate content and a clean URL structure.

There’s a helpful nuance for those of us who like clean code: Google notes that you don’t need perfect semantic HTML. The web in general isn’t valid HTML, and Google can interpret what you’ve got [1]. Use semantic HTML where you can — mostly because it helps real users, including screen readers, assistive tech and, increasingly, browser-based AI agents.

#3. Create non-commodity content that only you can produce

This is where the guide gets a little philosophical — and where I think it speaks loudest to academics, researchers and technical experts.

Google introduces a useful phrase: non-commodity content [1]. Commodity content is the kind anyone (or any LLM) could produce — a generic “7 tips for first-time homebuyers”. Non-commodity content offers a viewpoint that can’t be cheaply replicated: a first-hand account, an expert teardown, a counter-intuitive case study from your own work [1]. google

Google’s quick test is worth memorising: is this content my visitors would find satisfying? If yes, you’re on the right track [1, 3].

#4. Skip the hacks: no llms.txt, no chunking, no fake mentions

This is the section that quietly dismantles a lot of the “GEO toolkit” being sold right now.

Google explicitly lists what you do not need to do for AI search [1]:

• No llms.txt files or special AI markup. Google does not give these any special treatment for Search ranking. You can safely ignore them as a Google visibility lever.

• No “chunking” content into tiny pieces. Google’s systems already understand multiple topics on a single page. There is no ideal page length — write for your audience, not for an imagined LLM parser.

• No rewriting for AI. Google’s systems understand synonyms and meaning. You don’t need to chase every “long-tail” variation of how someone might phrase a question.

• No buying inauthentic mentions. Mentions on low-quality, low-traffic sites don’t help, and Google’s spam systems are designed to filter them out [1, 4].

• No over-engineering structured data. Structured data is still useful for rich results in classic Search, but it isn’t a requirement for generative AI features [1].

For anyone who has been pitched a “GEO audit” that mostly amounts to generating an llms.txt and seeding mentions across obscure forums — this is your permission slip to politely decline.

#5. Look ahead: agentic experiences are coming

The final takeaway is more of a heads-up than an instruction.

AI agents — autonomous systems that can book a reservation, compare specifications or pull data on a user’s behalf — are starting to interact with websites directly [1]. They may render pages, read the DOM and inspect the accessibility tree to complete tasks.

You don’t need to overhaul your site for this today. But two existing investments pay off here:

• Semantic HTML and a strong accessibility tree make your content easier for agents to parse — and easier for assistive technologies, which has always been the right call regardless.

• Clear page structures and well-labelled interactive elements help agents understand what your site actually offers.

Emerging protocols like the Universal Commerce Protocol will likely shape how Search agents interact with sites in the coming years [1]. Worth keeping on your radar, particularly if you run an ecommerce or booking-based business.

What this means in practice

If you take one thing from this guide, take this: good SEO and good content are the strategy. Not a hack. Not a separate AI playbook.

In practice:

• Keep your site technically clean and fast.

• Write from your actual experience and expertise — the kind of content that can only come from you.

• Structure your pages for humans first.

• Resist the “GEO hacks” being sold around llms.txt and content chunking.

• Stay aware of where agentic search is heading.

If your foundations are solid and you write for your target audience by adding genuine value — you’re already optimising for generative AI search. The work isn’t different. It’s the same work, done well.

Frequently asked questions

• Is SEO dead because of AI search? No. Google’s official guidance is that generative AI features in Search are built on the same ranking and quality systems as classic Search. Solid SEO foundations remain the best way to be visible in AI Overviews and AI Mode [1].

• What is the difference between SEO, AEO and GEO? From Google’s perspective, there isn’t one. AEO (answer engine optimisation) and GEO (generative engine optimisation) describe the same goal — being visible in AI-powered search experiences — but the underlying work is SEO [1].

• Do I need an llms.txt file for my website? Not for Google Search. Google does not require or give special treatment to llms.txt files for visibility in generative AI features [1]. It may still be useful for some third-party tools, but it shouldn’t be your priority.

• Should I “chunk” my content into smaller pieces for AI? No. Google’s systems understand multiple topics within a single page. There is no ideal page length — focus on writing for your readers [1].

• Does structured data still matter? Structured data isn’t required for generative AI search, but it remains useful for rich results in classic Google Search. It’s worth keeping in your SEO toolkit, just not at the expense of strong content and a clean technical foundation [1].

• What should I do first if I want to be visible in AI search? Audit the basics — indexing, page experience and content quality. Then ask the simple test Google itself suggests: would my visitors find this content satisfying? If yes, you’re on the right track [1, 3].

References

[1] Google Search Central. (2026). Optimising your website for generative AI features on Google Search. Last updated 15 May 2026. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

[2] Google Search Central. SEO Starter Guide. https://developers.google.com/search/docs/fundamentals/seo-starter-guide

[3] Google Search Central. Creating helpful, reliable, people-first content. https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[4] Google Search Central. Spam policies for Google web search. https://developers.google.com/search/docs/essentials/spam-policies

Go further

1/ Explore how I help scientists build strategic communication to showcase their experience and build their authority online → Visit tiffanysiegel-sci.com
2/ Follow along for weekly strategic insights → Connect on LinkedIn

They’re searching Google. And if your work doesn’t show up, it doesn’t exist — not for them.

A blog article is your translation layer. It’s your research rewritten for discoverability and accessibility, in terms a non-specialist can engage with. It bridges the gap between your peer-reviewed publication and the people who could fund, commercialise, or implement your findings.

However, there is a perk. AI tools promise to make this faster and easier — and they can. But for researchers and technical experts, your voice is your credibility. A blog article that reads like it was generated by ChatGPT doesn’t just sound generic — it actively undermines the authority you’ve spent years building.

The good news? You don’t have to choose between optimisation and authenticity. This article introduces the D-R-A-F-T framework — a five-stage workflow that uses AI where it’s genuinely useful, while keeping the thinking, the writing, and the final authority exactly where they belong: with you.

Why SEO matters for technical expert content

If you’ve published in a peer-reviewed journal, you’ve already proven your expertise to your discipline. But a journal article has a limited audience — specialists who know where to look and have access to the right databases. The rest of the world searches Google.

Search engine optimisation (SEO) is how you make your content discoverable to that broader audience. It’s the difference between a brilliant article that sits in obscurity and one that reaches the funders, journalists, industry partners, and policymakers who need to see it.

Here’s where researchers have a natural advantage. Google’s current ranking framework prioritises something called E-E-A-T: experience, expertise, authoritativeness, and trustworthiness [1]. This isn’t a technical trick — it’s a quality signal. Search engines are asking: does the person who wrote this actually know what they’re talking about? Have they done the work? Can they be trusted?

If you’ve conducted original research, been peer-reviewed, and published in your field, you already have E-E-A-T in abundance. That’s your competitive edge — especially now, when AI-generated content is flooding the web and search engines are actively working to surface content with demonstrable human expertise [2].

The catch? Having expertise isn’t enough. It needs to be visible, structured, and accessible for search engines to recognise it and for non-specialist readers to benefit from it. That’s what optimisation does — not diluting your content, but making sure it’s structured for discovery.

So how do you optimise without losing what makes your content authoritative? With a workflow that keeps you in control.

The D-R-A-F-T framework: five stages from research to optimised article

The D-R-A-F-T framework is built on one principle: the human leads, AI supports. You bookend the process — defining the strategy at the start, fine-tuning the final output at the end — while AI handles the structural and technical layers in between.

D — Define (you)

R — Rough framework (AI scaffolds)

A — Author (you write)

F — Format and clarify (AI structures and clarifies)

T — Tune (you fine-tune)

Define (you)

You’ve already done the hard work. The research exists, it’s been peer-reviewed, it’s published. The question now is: who else needs to understand this, and what do they need to take away?

Start by defining your audience for this piece. Are you writing for potential investors? Government funding agencies? Industry partners exploring collaboration? Science journalists? The general public? Each audience changes the language, the depth, and the angle.

Then define your key argument — the “so what?” in terms your target reader cares about. This isn’t the same as your paper’s abstract. A funder wants to know potential impact. An industry partner wants to know practical applications. A policymaker wants to know relevance to current challenges.

Finally, think about intent. What is your target reader actually searching for? What question are they typing into Google that your article could answer?

This stage is entirely human. AI cannot define your strategy. It doesn’t know your field, your audience, or what makes your research significant beyond the published record. These decisions — the scope, the angle, the purpose — are yours.

Pro tip: Type your topic into Google and look at the “People also ask” box. Those questions are real queries from real users. They’ll tell you what non-specialists want to know about your subject — and they’re free keyword research.

Rough framework (AI scaffolds)

With your strategy defined, you now have something concrete to hand to AI: a target audience, a key argument, and an idea of what your reader is searching for. This is where AI becomes genuinely useful — not as an author, but as a scaffolding tool.

Feed your rough ideas to an AI tool and ask it to generate an outline. Then use it as a Socratic tutor — ask it to challenge your structure, surface assumptions you might be making, and identify what a non-expert reader would need explained. What questions would a policymaker have that you’d take for granted as a scientist? What context is missing for someone outside your discipline?

This is also the stage to ask AI to suggest related subtopics or frequently asked questions. These become the seeds for your FAQ section and help ensure you’re covering the angles your audience actually cares about.

The guardrail here is clear: you accept, reject, or reshape every suggestion. AI proposes — you decide. The scaffold supports your thinking. It doesn’t replace it.

Pro tip: Be specific in your prompts. “Write an outline for a blog about my research” gives you generic output. Try: “I’ve published a paper on [X]. I want to write a 2,000-word blog article explaining the implications for [specific audience]. Here’s my key argument: [Y]. Suggest an outline covering the main points a non-specialist would need to understand, and challenge me on any gaps.” That gives AI something to work with — and gives you something worth evaluating.

Author (you write)

Now you write. In your own voice, from your own expertise, with your own perspective.

This is the translation step — taking your peer-reviewed findings and making them accessible without dumbing them down. You’re not writing a second journal article. You’re telling the story of your research in terms your target audience can engage with.

Don’t self-censor for SEO at this stage. Write for clarity and substance first. Focus on the argument, the evidence, and the implications. Use language your defined audience will understand, but don’t strip out the nuance that makes your perspective valuable.

Most importantly, include what AI cannot produce: your personal experience in the lab or in the field, your original analysis of what the findings mean, specific examples from your work, and the honest assessment of limitations that builds trust with informed readers. This is where E-E-A-T lives — in the human-written, experience-backed content that no prompt can generate.

Pro tip: Here’s a test for every paragraph you write. Ask yourself: could someone with access to ChatGPT or Claude and my paper’s abstract have written this? If yes, it’s not adding E-E-A-T value. The moments where you draw on what you’ve personally observed, tested, failed at, or concluded — that’s what makes your content irreplaceable.

Format and clarify (AI structures and clarifies)

With your draft written, it’s time to optimise the structure and technical elements — and this is where AI saves you the most time.

Heading hierarchy. Ask AI to review your H2 and H3 structure. Are the headings logically nested? Do they reflect the content of each section? Do they include terms your audience might search for? A well-structured heading hierarchy helps both readers scanning the page and search engines understanding your content.

Metadata. Use AI to draft your meta title (the headline that appears in search results) and meta description (the short summary beneath it). These are critical — they determine whether someone clicks on your article. AI can generate options quickly, and you choose the one that’s accurate and sounds like you.

Internal and external links. Ask AI to suggest where you could link to your own related content (building topical authority) or to authoritative external sources that support your claims.

Readability. Run your draft through AI with a readability lens. Flag overly long sentences, jargon that needs defining for your target audience, and transitions that could be clearer. This isn’t about simplifying your ideas — it’s about removing unnecessary friction between your reader and your message.

Structured data. AI can suggest schema markup for your article — the behind-the-scenes code that helps search engines categorise your content by type (article), author, date published, and more. You don’t need to implement this yourself if your CMS handles it, but it’s worth understanding what it is and ensuring it’s in place.

Grammar and punctuation. Finally, use tools like Grammarly or an AI-assisted proofreading pass for mechanical polish. Spelling errors and grammatical inconsistencies undermine the professionalism of otherwise excellent content. This is the last thing AI touches before you take back control.

Pro tip: Think of your meta description as your paper’s abstract, compressed to 155 characters. It needs to convey the core value of the piece and make someone want to click. AI can generate five options in seconds — your job is to pick the one that’s both accurate and sounds like something you’d actually say.

Tune (you fine-tune)

The final pass is always human. Always.

Go through every AI-generated suggestion and verify it. Does the heading AI proposed actually reflect what the section says? Is the meta description accurate? Did the readability edit remove nuance you intended to keep?

Fact-check any claims, statistics, or references — including your own. If you’ve let AI suggest external links or supporting evidence, verify every single one. AI fabricates citations. Your audience — fellow researchers and technical experts — will notice, and your credibility is not recoverable once lost.

Add your own views where the piece needs them. If there’s a section where your professional opinion matters — where the evidence is contested, where the implications are debatable, where your experience offers a perspective the data alone can’t — this is where you write it in.

Then read it aloud. Does it still sound like you? Would a colleague recognise your voice? If something sounds off, rewrite it. AI polish should be invisible — if a reader can tell which parts were AI-assisted, the process hasn’t worked.

Your name is on this article. You are the last authority.

Where AI helps vs. where it hurts

Let’s be honest about both sides. If you’re going to use AI in your workflow, you should know exactly where it adds value and where it introduces risk.

AI is good at:

• Generating structural outlines from rough ideas

• Drafting metadata options quickly

• Identifying readability issues and suggesting clearer phrasing

• Spotting gaps in topic coverage

• Handling grammar, punctuation, and formatting

• Suggesting internal linking opportunities

AI is not good at:

• Original insight or analysis

• Lived experience and professional judgement

• Nuanced argument in contested areas

• Accurate citations — AI fabricates references confidently and frequently

• Discipline-specific accuracy

• Your voice

The risk of over-reliance is real. If your finished article could have been written by anyone with access to an AI tool and your paper’s abstract, it carries no E-E-A-T value. Search engines are getting better at detecting this — and so are readers. An article that sounds like everyone else’s AI output won’t rank, won’t build trust, and won’t serve the purpose you wrote it for.

The principle is simple: AI amplifies expertise. It doesn’t replace it. Use it for the structural and technical layers. Keep the thinking, the writing, and the final judgement yours.

FAQs

• Can I use AI to write my blog articles entirely? You can, but you shouldn’t — at least not if you want the content to rank well and build your authority. Google’s guidelines distinguish between AI-assisted content (using AI as a tool within a human-led process) and AI-generated content (where AI is the primary author). The former is fine. The latter lacks the experience, expertise, and originality that search engines — and readers — are looking for [3].

• Which AI tools are best for SEO optimisation? This article is deliberately tool-agnostic. ChatGPT, Claude, Gemini, and others can all handle the scaffolding, structuring, and proofreading tasks described in the D-R-A-F-T framework. For grammar specifically, Grammarly and similar tools work well. For keyword research, free options like Google’s “People also ask” and Google Search Console are a strong starting point. The tool matters less than the workflow.

• How long should an optimised blog article be? There’s no magic number. Length should serve depth — write enough to cover your topic thoroughly for your target audience, and no more. For a research translation piece aimed at funders or industry partners, 1,500 to 3,000 words is typical. A shallow 500-word summary won’t demonstrate your expertise, but a 5,000-word essay may lose your reader before they reach your key findings.

• Do I need to know technical SEO to follow this workflow? No. The D-R-A-F-T framework covers the most impactful optimisation elements — headings, metadata, internal linking, and structured data — without requiring technical SEO knowledge. If your website is built on a modern CMS like WordPress, most of the technical infrastructure is handled by plugins and themes. Focus on the content-level optimisation described here.

• Will search engines penalise AI-assisted content? No — Google has explicitly stated that using AI as part of the content creation process is acceptable [3]. The distinction that matters is quality and intent. Content created with AI assistance, led by human expertise, and reviewed for accuracy is treated no differently from content written entirely by hand. Content that is generated at scale with no human oversight, no original insight, and no editorial review is what risks being deprioritised.

• How often should I update published articles for SEO? Content freshness matters, particularly for topics where research is ongoing. Review your published articles every six to twelve months. Update statistics, add links to newer related work, and revise any sections where your thinking or the evidence has evolved. Search engines notice when content is maintained — and so do readers who return to a reliable source.

References

[1] Google Search Central, “Creating helpful, reliable, people-first content,” Google Developers. [Online]. Available: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[2] Google Search Central, “Google Search’s core ranking systems,” Google Developers. [Online]. Available: https://developers.google.com/search/docs/appearance/ranking-systems-guide

[3] Google Search Central, “Google Search’s guidance about AI-generated content,” Google Developers, February 2023. [Online]. Available: https://developers.google.com/search/blog/2023/02/google-search-and-ai-content

First, some definitions

As fairly you don’t spend your days reading SEO trade press, a few terms in this piece might be new, so let’s start with some definitions before we go further.

What is a Google core update? It is a broad change to how Google decides which pages to show you when you search for something. Google itself describes core updates as recalibrations designed to keep search results helpful and reliable as the web evolves [1]. The official analogy on Google’s documentation is a nice one: think of your top-20 list of favourite restaurants. Some new restaurants open. Your tastes shift. The list reorders. Restaurants that move down aren’t bad — there are just other places now better suited to the moment [1].

It’s not a punishment for breaking rules. It’s the list being redrawn.

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It’s the framework Google uses to assess content quality. The simplest way to think about it: Google is trying to figure out whether the person who wrote a page actually knows what they’re talking about, and whether you can trust them. I’ve written about this before — it’s been the backbone of Google’s quality assessment since 2014, and the principles go back even further.

An aggregator is a website that collects and re-presents content that originates somewhere else. Think Glassdoor (job listings from employers), Trivago (hotel prices from hotels), Rotten Tomatoes (reviews from critics), or any “best of” listicle built entirely on other people’s products. Aggregators sit between you and the thing you actually want.

A primary source — or destination — is the opposite. It’s the original. The employer’s careers page. The hotel’s own website. The scientist’s published paper. The founder’s own product page. The thing the aggregator is pointing to.

That distinction — aggregator versus destination — is the single most important pattern in the March 2026 update.

What actually happened

Google’s first broad core update of 2026 lasted for 12 days (it began rolling out on 27 March and finished on 8 April) [2, 3]. It followed two other updates: a Discover-only update in February (the first of its kind), and a very fast spam update on 24 – 25 March that completed in under 20 hours [4, 5].

With three updates in six weeks — this could explain why your Search Console looked like a heart monitor for a while. This is what happened [4, 6]:

• Around 80% of pages that had been in the top three positions for a search query shifted positions. (For comparison, the December 2025 update moved about 67%.)

• Roughly one in four pages that had been on Google’s first page disappeared from the first hundred results entirely.

• More than half of websites being tracked saw noticeable ranking changes within the first fortnight.

Google’s official description was deliberately vague — a regular update designed to better surface relevant, satisfying content from all types of sites [3]. No companion blog post. No new guidance. The real story is in the patterns of who gained and who lost.

The three shifts that matter

Google rarely publishes a changelog. But based on visibility data and a fairly consistent trail across the SEO trade press, three shifts stand out.

1. Originality became a ranking factor

This is the biggest behavioural change, and the one I find most interesting.

Google now appears to actively reward pages that add something genuinely new to a topic — original research, first-hand experience, proprietary data, a unique perspective — over pages that simply rephrase what’s already out there [7, 8]. Long, well-structured, keyword-optimised summaries of what’s already on page one are losing ground. Pages with their own voice, their own data, their own findings are gaining.

The technical name for this is Information Gain — a concept Google has actually held a patent on for years. But March 2026 is the first update where its impact is clearly visible at scale. Early data suggests sites with proprietary research and expert commentary saw average visibility gains of around 22% [7].

If you’ve ever wondered why I keep telling scientists that their actual work — the research, the methodology, the first-hand findings — is the content asset, not the marketing collateral around it: this is why.

2. Authorship and credentials now carry algorithmic weight

What’s changed in March 2026 is the weight Google places on E-E-A-T signals. Two findings are worth pausing on [8, 9]:

• For sensitive topics — health, money, legal, safety (the so-called “YMYL” topics, i.e. Your Money or Your Life) — around 73% of top-ranking pages now display verifiable author credentials. Anonymous content is losing ground fast.

• Google’s systems now appear to cross-reference the named author of a page against external sources — LinkedIn profiles, speaker pages, publication history, citations. If you say you’re an expert, Google now checks.

That second point is the one that should make every domain expert sit up. The digital footprint you’ve built across LinkedIn, Substack, conferences, journal publications — Google can now use that as evidence of your expertise. It’s not just about what’s on your website anymore.

Google is also evaluating your whole site, not just individual pages. A site that goes deep on one topic outperforms a site that covers many unrelated topics [10]. A few thin pages can drag down the perceived authority of everything else you publish.

This is the part that matters most for the people I work with. If you’re a scientist or a domain expert, you already have the credentials. You already have the depth. You already have the consistency. You just need to make it visible in places Google’s systems can read.

3. AI content got filtered, not banned

Let me be clear, because this gets misreported every time: the update did not penalise AI-generated content as a category.

What it did is get much sharper at detecting content that was clearly produced at scale, with no real human review. Fluent prose that adds nothing new. Articles that read like they were written by a tireless intern who didn’t know the topic [8, 11].

The losers aren’t AI users. The losers are anyone — human or otherwise — who used a process to scale mediocre, undifferentiated content [11]. Think → AI → Think still works. AI → AI → AI doesn’t.

A useful reminder from John Mueller, Google’s Search Advocate: rewriting AI content by hand doesn’t fix it. If a site is built on low-value content, the strategy needs revising, not individual pages [12].

What content is actually getting promoted

The “three shifts” above explain the why. This section is the what.

Based on patterns observed during the rollout — particularly well-documented by SEO practitioners working closely with affected sites [12] — there’s a fairly clear list of content types and content elements that Google is now actively favouring:

• Real case studies. Not the kind with percentages pulled out of thin air. Documented examples with real outcomes — the kind a scientist or operator would recognise as evidence.

• First-hand experience. Concrete feedback from your own work. Things you’ve actually done, tested, or seen play out in practice. The “I did this and here’s what happened” structure.

• Annotated data and screenshots. Charts, dashboards, or data visualisations with arrows and callouts explaining what matters. Originals — not stock graphics.

• Expert input. Quotes or commentary from a recognised expert in your field. Or referencing your own (or others’) subject-matter posts on LinkedIn — Google appears to read those signals now.

• Genuinely deep, well-researched writing. Not AI-drafted prose nobody re-read. Content that engaged seriously with the question and didn’t just take the first answer ChatGPT or Claude offered.

• Topical authority on a specific niche. Supported by good backlinks — the kind earned through real credibility, not bought in bulk.

• Visible E-E-A-T signals. Author bios with credentials. Verifiable sources. Transparent attribution.

• Content written for readers, not for the algorithm.

And on the other side — content types that took a hit:

• Listicles. “Top 10...” style articles seem to have been deprioritised in many niches, possibly as part of a broader cleanup of overly promotional content [12].

• Mass-produced AI content. As discussed above — sites that scaled hundreds of generic AI articles are among the worst affected [6, 12].

• Anything obviously written for the algorithm rather than the reader.

If you’re translating this into your own publishing strategy: the format that wins right now is one expert, one specific question, one piece of original evidence, told properly.

Winners and losers

The visibility patterns map onto a single distinction, and it’s the one we set up at the start: destination versus aggregator.

Sites that gained ground

• Government and institutional sources (think Census.gov, BLS.gov) on fact-driven queries [4, 13]

• Specialist sites that go deep on one subject area [10, 13]

• Established brands with a strong direct presence in their space [13]

• Primary destinations — the employer’s careers page, the actual research publication, the original publisher [13]

Sites that lost ground

• Job aggregators like ZipRecruiter and Glassdoor — losing ground to the employers themselves [4, 13]

• Broad consumer health portals — losing ground to clinical sources and original research [13]

• Comparison sites and “best of” lists built on other people’s data [13, 14]

• Affiliate-heavy sites built around pushing you to buy something — among the hardest-hit overall [6]

• Mass-produced AI content with no editorial backbone [6, 11]

In one sentence: if your site sits between a user and the thing they actually want, this update was unkind. If your site is the thing they want, it probably worked in your favour [14].

What this means for founders

If you’re building a company, the message from March 2026 is simple: owned value compounds.

• Be a primary source, not a layer. Aggregators are losing ground to the destinations they used to send traffic to. If you’re a SaaS founder, that means investing in original product data, customer outcomes, proprietary research. Content nobody else can produce.

• Make your team’s expertise visible. Your engineers, scientists, operators are a competitive asset. Named author bios, LinkedIn-linked credentials, topic-consistent publishing — these now feed directly into how Google evaluates your domain.

• Treat brand as infrastructure. Strong brands won this update. People searching for your name directly, a real About page, verifiable contact info, consistent business listings — all of this feeds the trust component of E-E-A-T.

• Don’t outsource your moat. If your content strategy is “spin up an AI workflow and publish 200 articles a month,” March 2026 just turned that strategy from a shortcut into a liability.

The framing for any founder thinking about content investment in 2026: publish less, with deeper authorship. One genuinely original case study, methodology piece, or first-party benchmark will do more for your visibility than fifty thin posts.

What this means for tech experts

For technical professionals — engineers, researchers, security specialists, AI practitioners — March 2026 reinforces something I’ve been saying for a while: technical credibility is a content moat.

• First-hand experience markers matter. Phrases like “in my audits of…”, “when I tested…”, “across the deployments I’ve reviewed…” are now visible signals to Google’s quality systems [8]. Generic technical content without these markers is increasingly indistinguishable from scaled AI output.

• AI workflows need a human spine. Most of us are using AI to draft technical content — fine. But the content needs genuine expert review, original benchmarks, working code samples that someone actually ran. Without those, fluent prose is a liability, not an asset [11].

• Page experience is a baseline, not a differentiator. If you read SEO trade press, you’ll see references to Core Web Vitals — Google’s measurements of how quickly your page loads (LCP under 2.5 seconds), how responsive it feels when users click (INP under 200ms), and how stable the layout is as it loads (CLS under 0.1) [12]. These are now table stakes. Failing them is a ranking liability. Passing them is the floor, not the ceiling [8, 13].

• Structure for AI citation, not just ranking. AI Overviews, ChatGPT, Perplexity, Gemini — they cite the same kinds of content this update rewards: original, well-structured, expert-authored, clearly attributable. Research suggests roughly 44% of citations in AI tools come from the first 30% of a page’s text [9] — meaning your introduction and early sections carry disproportionate weight.

The consolation, if you’re a tech expert who’s been resisting the content-mill model: the algorithm has just made depth profitable again.

A practical playbook

If your traffic moved between late March and mid-April — or if you simply want to be in good shape for the next core update (likely June or July 2026 [11]) — here’s the disciplined response. This aligns closely with Google’s own published guidance on responding to core updates [1].

1. Wait at least a full week after the rollout completed before drawing conclusions. Google explicitly recommends this. The rollout finished on 8 April, so meaningful analysis starts from around 15 April onwards [1, 3].

2. Compare the right dates. In Google Search Console, compare a week of post-update data (mid-April onwards) against the week immediately before 27 March [1]. That gives you the cleanest signal of what changed.

3. Distinguish small drops from large drops. Google’s own guidance: a small drop (e.g. position 2 to 4) doesn’t need drastic action. A large drop (e.g. position 4 to 29) warrants a deeper assessment [1].

4. Analyse different search types separately. Did the drop come from regular Google search, Google Images, Video, or the News tab? They behave differently and may need different responses [1].

5. Confirm whether you’re looking at the core update or the spam update. A drop on or after 27 March points to the core update. A drop on 24 – 25 March points to the spam update. Some sites were affected by both [15].

6. Look at who gained in your niche, and what they’re doing differently. For your priority search terms, what do the new winners have? More first-hand experience? Original data? Named author with credentials? Deeper coverage of the topic?

7. Strengthen authorship. Add named author bios with verifiable credentials. Link them to LinkedIn and primary sources. Publish under named bylines, consistently [9].

8. Improve the content that needs improving — don’t panic-delete. Google’s official guidance is that deleting content is a last resort. Try improving or restructuring first. Only remove content if it’s genuinely unsalvageable [1, 10].

9. Invest in proprietary value. First-party data, case studies, original methodologies, in-the-wild experiments — these are what feeds the originality signal [7].

10. Get the technical floor right. If you have a developer, ask them to clear any “Poor” or “Needs Improvement” warnings in Google Search Console’s Core Web Vitals report. If you don’t, this is a reasonable thing to outsource [8].

The recovery window is real but not immediate. Google has stated that improvements can take effect within days, but often take several months — sometimes recovery comes only with the next core update [1, 11]. The work you do now compounds toward that next cycle.

The bigger picture

Each core update since late 2024 has tightened the bar. March 2026 tightened it further. The era of ranking on aggregated, repackaged, or thinly-edited content is ending — not suddenly, but steadily and consistently [14].

If you take one thing from this digest, take this: the algorithm hasn’t asked you to do anything new. It has simply made the thing scientists and technical experts have always done — work hard on something specific, tell the truth about it, put your name on it — into the dominant ranking signal.

That’s not bad news. That’s the playing field finally levelling out.

FAQs

• When did the March 2026 core update roll out? It began on 27 March 2026 and completed on 8 April 2026 — 12 days from start to finish [2, 3].

• Was it a penalty? No. Google’s own guidance is explicit: core updates are recalibrations of how the algorithm assesses content, not punishments for specific violations. A drop means other pages are now considered more useful for the affected queries [1].

• What is an aggregator, and why does it matter here? An aggregator is a website that collects and re-presents content from elsewhere — job sites that pull listings from employers, comparison sites that pull prices from retailers, “best of” listicles built on other people’s products. The March 2026 update consistently moved visibility away from aggregators and toward the original sources they aggregate [4, 14].

• What types of content did Google promote in this update? Real case studies, first-hand personal experience, annotated data and screenshots, expert input and quotes, genuinely deep research, topical authority on specific niches, and visible E-E-A-T signals (author bios, verifiable sources). Listicles and mass-produced AI content lost ground [12].

• Did it penalise AI-generated content? No, not as a category. It targeted content produced at scale without meaningful human review, regardless of who or what wrote the underlying draft [8, 11]. Expert-edited, AI-assisted content is performing well. Sites publishing AI content without human supervision are reporting visibility losses of 35 – 60% in early French analyses, with dedicated AI content farms hit harder still [6, 12].

• What is Information Gain? A Google concept (and patent) for measuring how much genuinely new information a page contributes relative to what already ranks. March 2026 is the first update where its impact has been clearly visible at scale [7].

• How is this different from the February 2026 update? The February 2026 update only affected Google Discover (the personalised content feed on Android phones and the Google app). March 2026 is a broad update affecting general search rankings globally [5].

• What should I do if my site dropped? Wait at least a full week after rollout completion (so from around 15 April onwards) before analysing. Compare the right dates in Google Search Console. Distinguish small from large drops. Look at what your competitors who gained are doing differently, strengthen E-E-A-T signals, and improve content where it lacks original value. Avoid wholesale rewrites or panic deletions — Google’s own guidance is that deletion should be a last resort [1].

• When is the next core update expected? Based on Google’s recent cadence, the next broad core update is expected around June or July 2026 [11]. Google also runs smaller, unannounced updates between major ones, so improvements can be recognised at any point [1].

  ---

References

[1] Google Search Central. (2025). Google Search’s core updates and your website. https://developers.google.com/search/docs/appearance/core-updates

[2] Search Engine Land. (2026, April 8). Google March 2026 core update rollout is now complete. https://searchengineland.com/google-march-2026-core-update-rollout-is-now-complete-473883

[3] Search Engine Journal. (2026, April 8). Google confirms March 2026 core update is complete. https://www.searchenginejournal.com/google-confirms-march-2026-core-update-is-complete/571459/

[4] Search Engine Land. (2026, April). March 2026 Google core update more volatile than December — here’s what changed. https://searchengineland.com/march-2026-google-core-update-what-changed-474397

[5] Search Engine Roundtable. (2026, April 8). Google’s March 2026 broad core update has completed rolling out. https://www.seroundtable.com/google-march-2026-core-update-complete-41145.html

[6] Quasa.io. (2026). Google March 2026 core update: Massive volatility, traffic winners and losers explained. https://quasa.io/media/google-march-2026-core-update-massive-volatility-traffic-winners-losers-explained

[7] Shortlist.io. (2026). Google’s March 2026 core update: What went down and how to adapt your SEO game. https://shortlist.io/blog/google-march-2026-core-update/

[8] ClickRank. (2026). Google March 2026 core update: What changed and what to do. https://www.clickrank.ai/google-march-2026-core-update/

[9] Launchcodex. (2026). Google March 2026 core update: What you need to know and how to adapt. https://launchcodex.com/blog/seo-geo-ai/google-march-2026-core-update/

[10] OrangeMonke. (2026). Google March 2026 core update: What you need to know. https://orangemonke.com/blogs/google-march-2026-core-update/

[11] Lifted Websites. (2026). March 2026 Google core update: What actually changed. https://liftedwebsites.com/march-2026-google-core-update/

[12] Bart, A. (2026, March 21). Mise à jour Google Mars 2026 — On décrypte + guide de survie. Apprendre le SEO sans migraine.

Apprendre le SEO sans migraine 💊

🤖 Mise à jour Google Mars 2026

Comme une habitude qu’on n’aime pas trop. Toutes les années en Mars c’est reparti pour un tour, la grosse mise à jour de Google qui porte le nom de “March Core Update…

Read more

4 months ago · 47 likes · 6 comments · Amandine 🦦

[13] YellowJack Media. (2026). Google March 2026 core update: What changed. https://www.yellowjackmedia.com/google-march-2026-core-update-what-changed/

[14] Opositive. (2026). Google March 2026 core update: Volatility, winners and losers. https://news.opositive.io/google-algorithm-update/google-march-2026-core-update-volatility-winners-losers/

[15] ALM Corp. (2026). Google March 2026 core update: Confirmed timeline, SEO impact, and what site owners should do next. https://almcorp.com/blog/google-march-2026-core-update/

Go further

1/ Explore how I help scientists build strategic websites → Visit tiffanysiegel-sci.com

2/ Follow along for weekly strategic insights → Connect on LinkedIn