We’re learning something new about getting LLMs to notice our content – it’s not as mysterious as we thought. Our team’s been watching the patterns, and we’ve found some interesting stuff.

We’re not just throwing content out there anymore. We’re making sure our stuff shows up where both machines and people are looking. Using LLMs ourselves, we’re testing different versions fast – from quick hooks to deep 1,800-word pieces. We’re watching what sticks.

Our content’s got to match what people want – whether they’re looking for info, ready to buy, or just browsing around. We’re taking our best blog posts and turning them into threads, FAQs, tweets, LinkedIn posts, even video scripts. Each one’s got our keywords and topics woven in naturally.

Key Takeaway

  • We’re building our content in tight clusters that make sense for LLM-aware SEO
  • We’re taking our content global, but we’re doing it carefully with proper translation work
  • We’re watching trends and sentiment like hawks, but we’re keeping human eyes on everything to make sure it sounds real

Expanding Our Reach with Creative and Multilingual Content

How We’re Transforming Our Text

Nobody reads the same way anymore, and we get that. We’re breaking down our content so it actually works for people.

When we write a 1,500-word guide about LLM SEO, we’re turning it into:

  • 5 social posts with targeted hashtags (we track every single link)
  • 1 LinkedIn deck, keeping it tight at 6 slides (we stick to 40-60 words per slide)
  • 2 short videos with machine-readable captions (we do both vertical and horizontal)

We’re always testing our headlines and descriptions, watching our clicks and bounce rates, and making changes on the fly. We’re matching how our audience wants their information.

Getting Our Visuals Right

Let’s face it – we know people look at pictures first. That’s just how our brains are wired.

Our visuals aren’t just decorations. They’re driving shares, saves, and showing up in image searches. We pull the main ideas from our text and make them visual (we keep our charts in SVG, photos in PNG or WebP, all under 150 KB).

Staying within recommended image optimization guidelines like those outlined in recent digital content performance statistics helps us make sure every visual asset contributes to search visibility.

Here’s what we’re making:

  • 1 infographic showing three key stats
  • 2 square carousel images that explain our process
  • 1 main header image with detailed alt text

We’re not skipping the technical stuff either. Our file names use proper hyphens, our captions add value, and our alt text includes our main topic plus brand connection. These details are getting us picked up by other sites and PR teams. Good visuals travel fast.

Multilingual Content Generation for Global Markets

Credits: Hyein Yoon – Borderless Marketer

We’re not looking at AI mentions as some kind of system to game – we’re focusing on becoming the go-to source that AI naturally wants to reference. Think about it: when these language models need solid examples, shouldn’t our content be their first choice?

Our multilingual content teams aren’t just pushing translations through a machine. We’re crafting material that’s genuinely worth citing, whether it’s in English, Mandarin, or Arabic. Sure, we use AI to get the first draft going, but that’s just the beginning. Native speakers step in to give it that authentic feel, and we fine-tune everything from our headlines to our call-to-action buttons for each platform we’re targeting.

We’re building what we call our “entity relationships” across languages (that’s just our way of connecting related concepts so AI can follow along). When we see Claude starting to pick up our content more in, say, Germany, we don’t waste time – we pour more resources into that market right away.

Making Our Message Work Everywhere

Our international strategy goes way deeper than just translation. We’re adapting everything – switching between kilometers and miles, euros and dollars, and completely rethinking our cultural references. What clicks with Gemini’s U.S. audience might need a total overhaul for European users.

This is why we maintain detailed internal style guides supported by the kind of structured systems seen in the best internal documentation tools for teams in 2025, ensuring consistency across every market.

We’re super specific with our content guidelines:

  • Target market demographics
  • Phrases to avoid
  • Local statistics and measurements
  • Cultural touchpoints

We always test small first – maybe 10 social posts, a few headline options, one key image. If we see traction? That’s when we scale up big time.

Using Our Data to Make Smart Moves

Getting our language perfect isn’t enough – we’ve got to be strategic about where we place our content. Our team digs through the data to figure out exactly what these AI models are picking up. We’ve noticed they love content that’s organized in clear topic clusters, so that’s exactly what we’re building.

We keep close tabs on everything:

  • Weekly top 20 AI query reports
  • Monthly mention quality checks
  • Quarterly content refreshes

Watching How AI Talks About Us

We’re paying attention to the way these models discuss our work. When they start citing us as authorities, we lean into that with more case studies. If we notice any skepticism, we pivot to more detailed explanations.

Our testing process is pretty straightforward – we’ll put out two different content types and track which one gets more AI attention. Whatever performs better becomes our next week’s focus. No guessing games, just following what the data tells us works.

Catching the Right Wave at the Right Time

We’re always looking for overlap with our core stuff (LLM SEO, semantic keywords LLM, AI-powered search optimization) because that’s where we see the biggest impact. When a spike hits, we’ve got 24 hours to get a post live, and we treat every piece like a mini campaign.

That means smart distribution too, following proven approaches similar to strategic blog promotion methods that push content into both machine-learning discovery and human-driven channels at the same time.

Our daily routine’s pretty straightforward: 15 minutes scanning signals, one quick prompt for 5 headlines and 3 tweet threads that connect what’s hot to what we know. We’re always looking for overlap with our core stuff (LLM SEO, semantic keywords LLM, AI‑powered search optimization) because that’s where we see the biggest impact.

We move fast – when we spot a spike, we’ve got 24 hours to get a solid post up (usually 400-700 words), built for the way people actually search and talk.

Keeping Humans in the Loop

Look, we’ve spent enough late nights watching AI crank out content to know one thing – machines write fast, but they don’t get what really matters.

Our guardrails keep things on track:

  • Basic templates that give structure
  • Writing guidelines for consistent voice
  • Human eyes on every headline and fact

We’re old school about checking our work:

  • Fact and date verification
  • Real person readability check
  • SEO elements review

We let the machines handle the grunt work – first drafts, quick summaries, subject line variations – while our editors make sure everything lands right. Works pretty well for keeping the robot-speak out.

Making Sure Our AI Content Feels Real

We learned this one the hard way – you can’t fake authenticity. Everything we put out has solid sources and clear citations. We’re upfront about who wrote what and why, especially in our editor’s notes. [1]

For our bigger pieces, we’re transparent about our data sources and their limitations. When other writers can trace where our stuff comes from, they’re way more likely to share it.

Dodging the Generic Stuff

Nobody shares boring content. We keep things interesting by:

  • Finding angles only we can really own
  • Testing different storytelling approaches
  • Mixing technical deep-dives with real examples

Sure, we test headlines and button text, but what we’re really checking is whether each piece has something worth saying. When we start sounding like every other marketing blog, our mentions tank.

And yeah, we take the ethical side seriously. Has to be that way.

Our Ethics Come First

We don’t treat ethics like some add-on feature. When we’re working with AI, we keep it real – no made-up claims, clear labels when we use AI help, and we’re super careful with personal data (keeping everything properly separated, no weird data mixing).

We watch our language choices for fairness, make sure our translations stay true to meaning, and keep our bias in check. With our PR work, we’re straight-up with journalists and partners – probably why they keep mentioning us.

Our basic rules:

  • Give credit where it’s due
  • Check our data’s solid
  • Never make stuff up

Building Trust Every Step

We stack our content with trust signals – real author bios with actual credentials, links to our data sources, and notes when we update anything. If we’re using AI heavily in a piece, we say so and mention how our team reviewed it.

These little details matter – they help both AI and human readers feel confident sharing our work. Makes life easier for other editors too, which usually means more links our way.

Seeing Real Results from Our LLM Work

Looking through our analytics pile, one thing’s crystal clear – when we answer real questions clearly, that’s when ChatGPT and other LLMs pick us up. The numbers don’t lie.

We track our mentions across Claude, ChatGPT, and Gemini by watching referral patterns, search rankings, and how fast we’re getting cited. We keep our metrics simple:

  • How often do LLMs reference us?
  • How authoritative are those mentions?
  • Does our organic traffic follow?
  • Are people converting within a month?

Yeah, tracking attribution gets tricky with LLM mentions. We run 30-day multi-touch models and count assists, not just direct conversions. Not perfect, but it shows us what’s working.

How We’re Using These Tools

Our usage varies across teams. Some of us still use ChatGPT just for drafting. Others have completely changed how we work to create content that LLMs love citing. When we map it by department – PR, product, support – we can see where we need to improve.

We’ve noticed some content just naturally gets picked up in LLM training data, especially technical docs and support articles. We’re smart about it – starting small with stuff like meta descriptions, then expanding once we’ve got the right voice that works for both humans and machines.

Our ROI Numbers Tell the Story

We’re seeing that 68% ROI boost from AI tools isn’t just hype [2]. These tools are cutting our production costs while making our content easier to find – by people and machines alike. The math works out pretty simple – when we cut our drafting time in half and get more LLM mentions, our investment pays off fast.

We’ve got real examples from our work. Our B2B software team saw ChatGPT citations triple after we rebuilt content around clear technical explanations. When we reshaped our healthcare guides to focus on step-by-step clarity, Claude started picking them up regularly.

Our Quick Playbook

We’re keeping our process simple – deadlines don’t wait. Before we publish anything, we:

  • Build our topic clusters so LLMs can easily reference them
  • Use our tested prompts that drive citations
  • Test our winners in new markets (starting with 10 posts)
  • Mix up our formats across social, email, and visuals
  • Get our human editors to check accuracy
  • Push it out, promote it, watch what gets cited, and fix what doesn’t

Nothing complicated here. We’re just consistently putting out clear, useful content that LLMs want to reference. When we make it valuable for both humans and machines, our numbers follow.

Final Practical Advice

We think of LLMs as efficient tools for influence, not replacement for judgment. Start small, measure mentions and ROI, and scale where the data supports it. Keep humans in the loop for nuance and ethics, and localize rather than translate if you want international traction.

If we want mentions, we write for the places people search and talk, then make our content easy to cite and share. Try this: pick one pillar topic, generate three angles with prompts, localize one angle, produce a visual, and publish within two weeks. Watch mentions, adjust, and repeat.

If you want a ready-to-use prompt template or a 30-day content plan that ties LLM SEO tactics to mention metrics, we can send one. Tell us which industry and target markets matter most to you, and we’ll tailor the plan.

References

  1. https://www.ibm.com/think/topics/ai-transparency
  2. https://www.mckinsey.com/~/media/mckinsey/industries/technology%20media%20and%20telecommunications/high%20tech/our%20insights/beyond%20the%20hype%20capturing%20the%20potential%20of%20ai%20and%20gen%20ai%20in%20tmt/beyond-the-hype-capturing-the-potential-of-ai-and-gen-ai-in-tmt.pdf

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Frequently Asked Questions

LLM SEO controls how search engines and AI search pull your brand into AI Answers and search results. By optimizing Structured Data, semantic keywords and entity associations for large language model pipelines and AI platforms (ChatGPT, Gemini), you increase brand mentions and brand visibility in Google search, AI Overviews and Share of Model platforms, driving referral traffic and improving overall search engine discovery.

Content authenticity signals trust in AI-powered metadata: accurate Structured Data, mapped entities and semantic clustering tie long‑tail keywords and brand language to authoritative sources and public knowledge. 

That consistency helps AI companies and Google’s AI Overviews reuse your content in AI Answers, reduces citation policy risks, and boosts discovery across blog posts, media coverage and social media while preserving brand visibility.

Chunking content into concise thematic sections improves readability for humans and LLMs, enabling cleaner AI Answers, better summarization and keyword reinforcement. 

This practice helps generative platforms and search engines surface correct brand mentions in PAA questions and search queries, increasing referral traffic and scalability for content ecosystems and solution‑oriented websites across Gen AI platforms and traditional Google search.

Measure brand mentions in AI-generated answers and SERP data to close the LLM SEO loop: track Google Rankings, Bing rankings, Share of Model platform exposure, and referral traffic. Pair entity mapping and semantic search optimization with A/B-tested prompts, PR efforts and digital PR to improve citations. 

Regular performance tracking and Market research collection inform prompt refinements and increase authoritative presence; practitioners like Kate Giove emphasize measurement-driven strategies.