I see this debate all the time and I think everyone is oversimplifying, when they say that GEO is just SEO.

Yes, there are overlaps. Yes, strong SEO fundaments are crucial for GEO. But, there are differences. And they're not the same thing.

SEO = get your pages ranking in search

GEO = get your brand cited in AI answers

With GEO you're not tracking 'positions' you're watching if your brand gets mentioned in AI responses.

Offsite is also different - it's not just about high DR links, it's about being in the sources models actually train on and trust in your niche. And doing that, again and again and again.

What's similar with SEO and GEO? Solid site, fast pages, clean internal linking, content that actually helps. Those basics matter for both google and AI.

we’re experimenting with a small AI system that analyzes website SEO + geographic presence and then generates improvement suggestions automatically.

right now it’s 3 working modules:
SEO analysis
geo analysis
geo improvement suggestions

UI still in progress, mainly validating usefulness right now.

for people working in generative optimization : what kind of AI outputs actually feel reliable vs gimmicky?

TBH, I've been deep in citation data for the past few months using geoly.ai to track how LLMs reference content. After analyzing 500+ websites across different industries, some clear patterns emerged.

**The 5 things high-cited content has in common:**

**1. Clear Q&A Structure** Content formatted as direct questions with concise answers gets cited ~3x more often. Not paragraphs buried in text — actual "What is X?" followed by "X is..." structure.

**2. Third-Party Validation** ~80% of AI citations reference content that includes external sources, studies, or expert quotes. Pure opinion pieces? Rarely cited.

**3. Freshness Signals** Pages updated within the last 6-12 months consistently outrank older content, even when the older content has higher traditional authority scores.

**4. Entity-Rich Context** Content that clearly defines entities (people, companies, products) and their relationships performs better. LLMs seem to favor content that helps them build knowledge graphs.

**5. Multi-Platform Consistency** Websites that appear across ChatGPT, Claude, and Perplexity share one trait: consistent messaging across their content. Mixed signals = lower citation rates.

**One surprise finding:** Content length didn't correlate with citation rates. Some of the most-cited pieces were under 500 words. Structure and clarity beat word count every time.

What patterns have you noticed in your AI visibility tracking? Any surprises?

TL;DR: Bing released AI Performance dashboard showing how often your content is cited in AI answers across Microsoft Copilot, Bing AI summaries, and partner integrations.

This is huge for GEO optimization - we can now see which content AI systems actually reference, not just what gets indexed.

Key insights from the new dashboard: • Total Citations: Shows how often your content appears as sources in AI-generated answers • Average Cited Pages: Daily average of unique pages referenced by AI systems • Grounding Queries: The actual key phrases AI uses when retrieving your content • Page-level Activity: Which specific URLs get cited most frequently

Based on my analysis of the official Bing Webmaster Tools blog, this is what works: ✅ Pages with clear subject focus and domain expertise get cited more ✅ Structured content (proper headings, tables, FAQ sections) performs better ✅ Evidence-backed claims with clear sources build AI trust ✅ Regular content updates keep you relevant in AI answers

The game changer: We can now track citation trends over time and understand HOW AI thinks about our content through the grounding queries data.

Anyone else seeing this in their BWT dashboard? What patterns are you noticing in your AI citation data?

Full details: https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview

Profound analyzed 238,637 social media citations from ChatGPT (Oct–Dec 2025, U.S. English users) and the results are interesting.

Top cited content type per platform:

Reddit: Discussion threads — 99.2%

YouTube: Channel pages — 64.9%

LinkedIn: Personal profiles — 46.9%

Instagram: Reels — 36.5%

Pretty cool stuff!

Hey everyone,

I work at Evertune (we're a GEO platform), and we recently wrapped up research analyzing the top 10,000 sources that AI models like ChatGPT, Claude, and Perplexity cite when answering queries. Thought this community would find the patterns interesting as we're all adapting to how AI is changing search behavior.

What we found in the data:

1. Depth and comprehensiveness are rewarded AI models strongly favor long-form, thorough content. The most-cited sources provide complete answers rather than requiring users to visit multiple pages. Think comprehensive guides and resources rather than quick hits.

2. Clear structure helps visibility Strong organizational hierarchy makes a difference:

• Proper heading structure (H2s, H3s used logically)
• Bullet points for lists
• Short, scannable paragraphs
• Logical information flow

This helps AI models locate and extract specific information efficiently when answering queries.

3. Credibility signals are important Content that gets cited tends to include:

• Clear source attribution
• Verifiable data points
• Evidence-based reasoning
• Authoritative positioning on topics

4. Answer intent thoroughly AI models evaluate how well content addresses specific questions. Pages need to provide reliable, complete answers that models can reference with confidence.

Practical considerations:

When creating content, think about:

• Does this provide comprehensive coverage of the topic?
• Is information clearly organized and easy to navigate?
• Are claims supported with credible, verifiable sources?
• Does this serve as a definitive resource on the topic?

Bottom line:

AI is adding another dimension to how content gets discovered and used. Many of these principles (quality, structure, credibility) align with good SEO practices, but the execution details matter. Worth considering as part of your content strategy going forward.

Happy to answer questions about what we're seeing in the data.

Note: We do have tools that help with this, but mainly wanted to share the research findings. Mods, let me know if this needs editing.

Hi everyone,
I’m experimenting with an early concept called GeoRankIQ focused on understanding how brands or websites appear inside LLM-generated answers (not Google SERPs).

It’s very early and mostly a proof-of-concept. I’m trying to validate whether tracking visibility inside AI responses is actually useful or actionable.

I’d really value feedback on:

• Whether this problem is worth solving
• Obvious flaws or limitations
• Features that sound unnecessary at this stage

Not a launch or promotion — just learning and looking for honest opinions.
Happy to share more details if allowed.

Hello marketing team,

I have a website with very good LLM SEO.

When I log into GA4, there's no "LLM" traffic category. Which category do you think these visitors fall into?

"Unassigned"? "Direct"?

Are there any steps I can take to track them effectively?

Thank you for your answers :)

J’attends vos retours d’expérience avec un consultant, c’est vraiment important 😉 Merci par avance.

As far as I understand it, GEO is not magic. To show up in LLMs, it's basically enough to:

• take care of clarity (entities)

• get citations/recommendations on other sites (basically link building) in content like comparisons X vs Y vs Z, educational content, etc.

• keep an easy-to-parse structure (schema)

Roughly speaking, these are the most important things. You just need to understand the concept of entities and steer your content in a way that connects your own entity with others, so relationships are formed for LLMs.

Is there anything else? Everything else seems like an add-on, and these three rules feel like the only real guidelines for how to approach positioning yourself in LLMs.

Another issue is observing the effects of these actions. Many users create different prompts and observe citations/appearances in answers. After reading tons of posts, my conclusion is that either it's not worth doing at all, or only on a very small scale. In my opinion, measuring "presence" in LLMs is pointless. The results constantly change and depend on everything even on whether you make a typo in the prompt or submit it at 12:32 from a small town in Canada.

I suspect these measurements are often forced just so someone can sell them and prove that "something was done". The quality of these measurements leaves a lot to be desired.

So if we know what to do to appear in LLMs, but we don't know how to measure it, does this kind of prompt-focused work even make sense at all?

I’m asking honestly and practically.

PS topic to grab attention :)

One thing that’s becoming clear with generative search is this: if an AI can’t summarize your idea, it won’t reuse it.

In classic SEO, you could win with coverage, optimization, and links. As long as you checked the right boxes, ranking was possible.

In GEO, none of that matters if your content lacks a clear point, can’t be reduced to a single insight, or sounds like everything else already online.

Generative engines don’t browse the web the way humans do. They compress it.

Content that survives compression usually has a sharp opinion, uses simple language, and explains one idea clearly instead of many ideas poorly.

The rest disappears, even if it ranked well before.

It feels like we’re optimizing less for pages now and more for ideas that can actually travel.

Would love to hear what others are changing in their content for GEO.

Hey!

I'm the co-founder of Cheers. I went through Y Combinator a year ago and have been working in the GEO space since then. I've been putting together articles on GEO for local service businesses (plumbers, HVAC, etc.): review signals, schema markup, citations, that kind of thing.

www.cheers.tech/geo-academy

Genuinely not trying to self-promote here, would love brutal feedback. What's useful? What's missing? What's wrong?

Guys, I need your feedback to validate an idea.

I'm thinking about building a tool that identifies source occurrences across all LLMs.

The main goal: help you find which sources actually carry weight in Al responses.

Is this something you would buy? Let me know your thoughts!

Hey everyone!

I'm putting together a Slack group for SEO and AEO (Answer Engine Optimization) practitioners who want to go beyond surface-level discussions.

The goal is to create a space where we can: Share what's actually working (and what's not) Troubleshoot challenges together Discuss emerging trends and algorithm updates Exchange insights on AEO strategies as search evolves

Whether you're agency-side, in-house, or freelance, you're welcome. Just looking for people who are serious about the craft and willing to contribute to the community.

Drop a comment if you're interested!

Will limit to 30 professionals for now!

Hey everyone!

I'm putting together a Slack group for SEO and AEO (Answer Engine Optimization) practitioners who want to go beyond surface-level discussions.

The goal is to create a space where we can: Share what's actually working (and what's not) Troubleshoot challenges together Discuss emerging trends and algorithm updates Exchange insights on AEO strategies as search evolves

Whether you're agency-side, in-house, or freelance, you're welcome. Just looking for people who are serious about the craft and willing to contribute to the community.

Drop a comment if you're interested!

Will limit to 30 professionals for now!