Digital Insights

If you are small business, it can be really hard to stay on top of social media. You could hire someone, but the decision is often a trade off between time and money.

With additional pressure being applied by tariffs, small business owners need to get more out of the channels they have available to them . . . and social is a very important one.

With time constraints, there is a lot of pressure to post the same content on every channel. Although using the same visuals on different channels is sometimes needed because of cost of time, writing content geared toward your audience on each channel is a great use of AI.

By using ChatGPT and giving it specific instructions, you can generation a couple of options that are tailored to the platform and the content you are trying to promote.

When you create content for social platforms, you can tailor them for the type of format and audience. This is a very general prompt. If you have a target within the platform – SEO professionals in my case – then you can add that to the information by including that detail in the prompt.

I ask for multiple options and both short and long formats. Why? It helps to generate different versions to get one that is the closest to what you are trying to communicate.

If you have a PRO version, you can do it in a project that has your brand guide. Don’t have a brand guide, just put a few sentences in a word document to describe yourself and use it for a consistent reference.

THE PROMPT

please write individual social media posts for instagram, facebook and linkedin to grab attention for each audience on those platforms for the following blog [URL]. please provide 2 options in both short and longer format.

THE RESULT

Here are attention-grabbing social posts tailored to each platform’s audience for the blog “The AI Citation Factor That Matters Most: Clarity Over Cleverness” from Digital Insight Labs — with both short and long formats in two options each:

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Instagram (Audience: Visual, quick impact)

🟣 Option 1 — Short
Clarity wins. 🧠📊 AI search isn’t impressed by clever fluff — it rewards clear, direct answers first. Want better visibility in AI-powered results? Lead with clarity, not complexity. 🔍✨
#AICitation #ContentStrategy #AIsearch

🟣 Option 2 — Short
AI reads differently than humans. 📖➡️🧠 Cut the jargon. Serve the answer. That’s how your content gets picked up and cited by generative AI. Ready to rethink your content playbook? 💡🙌
#AIContent #DigitalMarketing #ClarityOverCleverness

🟣 Option 1 — Longer
If you’re creating content for the AI era — this one matters. 📌 A study of 300k+ URLs shows that generative AI platforms prioritize clarity and summarization over tricks like keyword stuffing or backlinks. The takeaway? Straightforward, audience-first answers are what AI search engines actually cite and surface.
✨ Human judgment in structuring, simplifying, and leading with answers beats clever spins every time.
#ContentTips #AIstrategy #ClarityWins

🟣 Option 2 — Longer
Stop writing for robots and start writing for understanding. 🤝 New data proves generative AI isn’t after clever language — it’s after clarity. Content that explains what the reader needs in the fewest mental hops wins in AI search. This isn’t SEO vs AI — it’s humanity + clarity vs noise.
Lead with answers. Respect the reader’s time. Let AI discover you for the right reasons. 🚀
#ContentCreation #AIvisibility #MarketingInsights

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Facebook (Audience: Engaged, discussion-oriented)

🔵 Option 1 — Short
Why are some articles actually referenced by AI search tools? It comes down to clarity over cleverness. Simple, structured, answer-first content outperforms flashy language — even if it’s SEO optimized. Clarity is the new edge. 📈
Read more ↓

🔵 Option 2 — Short
If AI is part of your content distribution strategy — your best play might be to trim the fluff and sharpen the answers. The data says clarity correlates with AI citations more than backlinks, keywords, or fancy copy. 🤔
Want the full scoop? 📖

🔵 Option 1 — Longer
In the age of generative AI, content that answers first, then explains next, gets noticed — not the work with dense language or crafty SEO tricks. A massive analysis of AI citations shows that clarity and summarization are the strongest predictors of AI visibility.
The implication: your editorial thinking — not just your tech stack — is what will get you discovered.
Read the full post and rethink how you structure content for AI audiences. 🔗

🔵 Option 2 — Longer
AI search platforms are rewriting the rules of relevance. Traditional SEO signals still matter, but when it comes to AI citations, the leading factors are clarity, structure, and Q&A-style formatting. That means ditching ambiguity and leading with the answer your audience is actually seeking.
When AI prioritizes comprehension over cleverness, human editorial judgment becomes a major advantage.
Dive into the insights and adjust how you write for discovery in 2026 and beyond. 📘

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LinkedIn (Audience: Professional, strategic focus)

🟢 Option 1 — Short
New research on AI discovery shows something counter-intuitive: clear, direct responses beat clever language for AI citations. If you’re writing for AI search visibility, structure and clarity are your competitive differentiators. 🔍📊
#ContentStrategy #AIsearch #ThoughtLeadership

🟢 Option 2 — Short
In a landscape where AI influences visibility, answer clarity matters more than keyword density or backlinks. Leadership content that’s easy to parse and structured wins in AI-powered results.
Rethink how you convey value — with precision. 💡
#MarketingLeadership #AI #ContentOps

🟢 Option 1 — Longer
If your team is investing in content for AI search, this insight should shift how you work. The data from a large-scale AI optimization study shows clarity and summarization as the strongest signals correlated with AI citations — outpacing traditional SEO metrics like backlinks.
That elevates the role of editorial strategy: deciding what the audience needs to know first and delivering it with simplicity and structure.
It’s not AI vs humans — it’s human clarity helping AI surface better answers.
#StrategicContent #AIecosystem #DigitalGrowth

🟢 Option 2 — Longer
AI platforms may be transforming search, but the core of meaningful content hasn’t changed: serve the audience with clarity. The most cited content in generative AI isn’t the most verbose or keyword-stuffed — it’s the content that gets straight to the point and anticipates audience needs.
For content leaders, the strategic imperative now includes:
✔ answer-first structure
✔ clear summaries
✔ reduced cognitive load
This is where editorial judgment creates competitive advantage in AI visibility.
#ContentLeadership #AIstrategy #BusinessGrowth

What are the elements in your content that will help drive AI Citations.

The AI Citation Factor That Matters Most: ➡️ Clarity Over Cleverness ⬅️

After a recent AI Optimization study analyzing 1.2 million URLs and ~12K prompts, Semrush discovered something critical about AI search visibility: clarity and summarization showed the strongest correlation with AI citations.

Not keyword density. Not promotional language. Not even backlinks.

Clear, direct answers.

What the data shows:

After analyzing 304,805 URLs cited by ChatGPT, Google AI Mode, and Perplexity against 921,614 URLs ranking in traditional Google search, five factors emerged as strong predictors of AI citations:

➡️ Clarity and summarization: +32.83%

➡️ E-E-A-T signals: +30.64%

➡️ Q&A formatting: +25.45%

➡️ Section structure: +22.91%

➡️ Structured data elements: +21.60%

So . . . if you need clarity, that is where this debate about using AI gets interesting for content strategists. Clarity and summarization aren’t technical SEO problems—they’re communication problems.

And that’s exactly where skilled human writers excel.

While AI writing tools can generate content at scale, human writers bring something essential to these top-performing factors:

➡️ The ability to identify what readers actually need to know first

➡️ The judgment to eliminate jargon and complexity without losing accuracy

➡️ The experience to structure information based on real audience comprehension patterns

➡️ The editorial instinct to lead with answers, not setup

AI can write, but even Sam Altman from OpenAI admitted the “Screwed Up” GPT-5.2 Writing Quality in their GPT-5.2 release. One of the reasons is that distilling complex information into clear, immediate value requires understanding your audience at a level that goes beyond pattern recognition.

The Practical Takeaway:

This isn’t about AI versus humans in content creation. It’s about recognizing that as AI search platforms prioritize clarity, the editorial skills that create clarity become more valuable, not less.

The content that wins in AI search is content that respects the reader’s time and cognitive load—something human editors have been trained to do for decades.

Your experience?

Where do you see human editorial judgment making the biggest difference in content that gets cited?

I recently was asking ChatGPT about the impact of content on the Reddit platform. It got me thinking about AI training and what is considered a ‘trusted’ source. The following is the response. Some great insights in this content.

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Trust scores aren’t a single “number,” but every major AI model uses multi-layered weighting systems during training to decide what counts as high-value knowledge and what gets downweighted or ignored. Here’s the straight explanation — no fluff.

How Trust Scores Work During AI Training

Think of trust scoring as signal weighting. Every piece of text is not treated equally.
Some content becomes a strong training signal. Some content becomes background noise. Some content gets thrown out.

These are the main factors models use to judge trustworthiness.

Source Reliability Scoring

Models (and their data pipelines) assign higher weight to content from:

• Peer-reviewed papers
• Government data
• Reputable news
• Authoritative publishers
• High-quality reference sites

Lower weight goes to:

• Ads
• Sponsored content
• Social media opinion
• Low-signal discussions
• Spam / SEO-bait sites

For example, Reddit is considered medium-trust.  The AI engines consider it useful, but not authoritative. During training, high-authority text shapes the model more heavily.

Internal Consistency Scoring

The text gets evaluated like this:

• Does it contradict known facts in the high-authority corpus?
• Does it contain verifiable claims?
• Is the writing coherent or chaotic?
• Are arguments logical or wildly inconsistent?

Content that contradicts strong, well-established knowledge gets downweighted.

Sponsored content tends to get downweighted because it:

Makes unverified claims

Pushes a narrative

Often includes emotional or marketing language

Pattern + Redundancy Scoring

If thousands of independent sources say the same thing → high trust.

If only marketing materials say it → low trust.

Models look for convergent truth across the dataset.

Sponsored content rarely converges with neutral sources, so it rarely gains weight.

Metadata Scoring

Even if you never see it, training data comes with context signals:

• Domain reputation
• Link patterns
• Post history
• Age of domain
• Author credibility (when known)
• Presence of affiliate tags
• “Sponsored,” “#ad,” or promotional markers

All of these influence the trust weighting before training even begins.

Moderation Signals

On platforms like Reddit:

• Highly upvoted, non-controversial threads → more trust
• Highly downvoted, flagged, or mod-removed content → downweighted or excluded

Sponsored content tends to have:

• Lower engagement
• More skepticism
• More negative signals → leading to lower trust scores.

Model Self-Consistency (post-training)

During reinforcement stages (RLHF or similar), evaluators check:

• Does the model repeat unverified marketing claims?
• Does it spread misinformation?
• Does it give unsupported assertions?

When the model “trusts” bad data, it gets corrected.

This feedback further suppresses unreliable or sponsored signals.

Deduplication and Noise Filtering

Massive datasets often contain:

• Duplicates
• Ads scraped accidentally
• Spam
• SEO-stuffed affiliate content

De-dupe filters act like an additional trust layer.
Sponsored content is often:

• Duplicate across many sites
• Template-based
• Identified as LLM-generated or spammy

These get automatically deprioritized.

Putting It All Together

During training, a Reddit post with “#ad” or sponsored language will:

• Not be excluded (it’s still public content)
• But be heavily downweighted compared to trusted sources

A government report might get weight = 1.0
A well-researched article might get weight = 0.6
A normal Reddit comment might get weight = 0.2
A sponsored post might get weight = 0.05
An ad = 0 (excluded)

That’s the basic trust landscape.

Bottom line

AI models don’t “trust” sponsored content.
They ingest it, but treat it like background noise.
It almost never influences model knowledge, and it never shows up as a cited source.

You and your team have been working tirelessly on getting those high value SEO links. Looking for opportunities, building relationship and helping your site authority to maintain its position or grow.

For me, I partnered with many PR teams go get links to the website. I used to have a saying when site would give a link, but make it NOFOLLOW. Well, the search engines might not follow it, but human beings will.

As go the human beings, go the robots.

Do Backlinks Matter in AI Search?

In a new AI Search Backlinks Study by SEMrush, the initial assessment is that “backlinks are important for AI visibility, but quality and authority matter far more than quantity.”

The team looked at 1,000 domains using their AI Visibility Toolkit to determine what influence backlink-related signals had in AI-generated responses. They included ChatGPT, ChatGPT with Search, Gemini, Google AI Overviews and Perplexity in the platforms they evaluated.

The review included looking at the number of AI mentions (how many times a domain was referenced across AI-generated answers) and AI Share of Voice (a domain’s visibility in AI-generated answers relative to non-branded queries) against key backlink signals including total links, number of referring domains and Authority score.

Authority Score had the Biggest Impact on AI Citations

Domains with higher Authority Scores—a metric heavily influenced by backlink quality and referring domain diversity—are more likely to appear in AI-generated answers. Although it might be the best performing, it had a modest impact to the performance overall.

That means that a lot of small link won’t move the needle that much. To move up, you need to earn backlinks from higher-authority, topically relevant sites. This is when PR comes into play. Earn media placements from well-known publications, leading blogs, or industry directories have higher authority and credibility. They make a bigger impact on performance.

Link Quality Drives AI Visibility More Than Volume

Sometimes . . . more is just more. It is a mantra that I have always thought was important in link building. Lots of links for OK sites won’t buy you that much and take a lot of effort to maintain over time.

In the AI Search world, the SEMrush research study shows that quality of backlinks plays a major role in how often a domain appears in AI-generated answers.

If you domain has high-quality backlinks, that usually translates into appearing more often in AI-generated answers, especially once they cross key authority thresholds.

Why do I say usually, the effect varies across models: ChatGPT Search and Perplexity weigh link quality the least. However, standard ChatGPT (without Search on) shows the strongest correlation.

Nofollow Links Carry Almost as Much Weight as Follow Links

Do you remember all of that time you spent slightly bummed out that you landing a great link, but it was NOFOLLOW. Well, that tide has turned in the AI world.

I had always wondered why Google and other Search engines even had such a ridiculous rule. It was like, I think your content is valuable and I will link to you, but I don’t link you enough to let the search engine like you.

Don’t forget getting a few high value links is the better is more links of mid or low quality. So basically, getting mentioned by an authoritative and relevant source is more important than chasing quick gains.

To read the full study and get details about the analysis of the study, view the study at: Do Backlinks Still Matter in AI Search? Insights from 1,000 Domains [Study].

With the rapid growth of AI Search and Generative Engine Optimization (GEO), the search world has been in a scramble to make sure they are getting their content ‘seen’ by users of these new tools including ChatGPT, Gemini, Claude, Perplexity, Copilot and others.

The State of AI Search

Most of the conversation has been around the evolution of content planning. It includes topics like:

• Using earned media for visiblity
• Migration to topic or query fanout discovery
• Strength of brand mentions
• LLM Prompt Tracking

It is definitely a good time to dive deep into the research, read every blog and attend as many webinars as possible to get details and insights about the way GEO and AI search will work.

For the most part, there is a big similarity to advanced tactics for SEO including creating topic clusters, linking building with earned content and using schema throughout a website.

All of these things track pretty closely from SEO into the transition to GEO.

The Potential Game Changer

I recently found an article about LLM perception match: The hurdle before fanout and why it matters. It talks in detail about LLM perception match (LPM). At first, I thought it was going to talk about how LLMs – ChatGPT specifically – have developed brand perception and the elements it takes into account.

I was stopped in my tracks by the opening sentence. “If LLMs don’t see you as a fit, your content gets ignored.”

It is like AI is vetting you to determine if they want to invite you to the party to be included in citations with a persistent evaluation of your brand / company. It asks questions like who you are, what you offer and how well you fit the user’s need.

How Your Evaluated

Before you will even be recommended, LLMs use a variety of sources to formulate a perception of your products and your brand. According to an AI visibility study from SEMrush and other research studies, they have found several ranking factors including:

• Website content
• Brand mentions (including associated sentiment)
• Customer reviews
• Competitor comparison
• Structured data
• Content freshness

This perception is persistent and synthesized. If it doesn’t align with the user’s intent, persona, or expectations, your brand is excluded before fanout ever begins.

According to Jessica Bowman who wrote the article about LLM Perception Match, “Simply put, LLM perception match is the gatekeeper. Without it, content quality and SEO don’t matter – you won’t even be in the running.”

Auditing Your Brand in LLMs

So with this potential barrier to LLM and AI Search inclusion, what do you do?

Take the time to audit your brand and determine perception based on the results. Is the AI engine saying your products are hard to use, your features are behind the times or you used to be the market leader, but are lagging in development?

One thing about ChatGPT or other AI engines is that they won’t hold back or sugar-coat the reality about your marketplace perception. If there are weaknesses in your supply chain, product offering or customer experience, AI will surface that.

It might make for difficult conversations with senior leadership and operations. This change is a signal that the days are when when you could write great content for a crummy company and still win the search game.

What It Means for Brands

It means that working on search and PR means being in lock step with senior management and directly integrated with operations. It also means that companies that have been able to avoid looking at their end-to-end performance will start to become invisible in the search world . . . at least until ChatGPT monetizes their tool and allows people to buy their way back in.