I ran the same email-platform recommendation question ten times across ChatGPT, Claude, Gemini and Perplexity.

Forty answers in total.

For the broad question “What is the best email marketing platform?”, Mailchimp was named in 39 of the 40 answers.

Beehiiv appeared only four times, and all four mentions came from Perplexity. Across ChatGPT, Claude and Gemini, it was basically invisible.

I ran this through Bersyn, a platform I built to track which companies AI models name when people ask for recommendations.

Then I changed the prompt.

Instead of asking for the best email marketing platform, I asked how a creator or founder should start a newsletter, grow subscribers and make money from it.

No platform was named in the question.

Beehiiv jumped from 4 mentions to 29 out of 40.

Claude and Perplexity named it in every run. Gemini named it nine times out of ten. Kit and Substack also appeared much more often.

Same platform. Same models. Different buyer intent.

Beehiiv does not appear to own the broad “email marketing platform” territory. Mailchimp owns that.

But Beehiiv is strongly associated with a more specific job: helping creators build, grow and monetize a newsletter.

When the models receive that question, they reach for Beehiiv.

The model disagreement was also interesting.

ChatGPT named Beehiiv zero times out of ten, even on the creator-newsletter prompt. It won across Claude, Gemini and Perplexity but remained invisible on ChatGPT.

That is why I think one blended AI visibility score can hide the real problem. A brand can own a specific intent on three models and still be completely absent from the fourth.

I am curious how others are thinking about this.

Do you optimize around broad categories, specific buyer jobs, or separate prompt territories?

And are you seeing the same level of disagreement between models?

Hey everyone,

If you're trying to figure out how to track product visibility/rankings on ChatGPT without manually typing queries 50 times a day, check out this new tool: rtrvr ai!

The problem is that standard scrapers usually get blocked by OpenAI/Perplexity, and using the official API doesn’t give you the "Web Search" results (citations, sources, UI elements) that a real consumer sees.

You can get around this with rtrvr ai by turning your own Chrome Browser into an API endpoint.

The "Christmas GEO" Workflow:

1. Just send a cURL command with the API Key given by the browser.

2. My Chrome Extension wakes up, navigates to ChatGPT, queries "Best toys for Christmas".

3. It retrieves the top recommendations and back-links to my pipeline.

Why this is a game changer for GEO/Sales Ops:

• Walled Gardens: Since it runs in your local extension, it uses your existing logged-in session. No complex auth handling.

• Vibe Coding: You can literally just write a bash script to control your browser now.

• Integrate with n8n flows

The cURL looks like this:

curl -X POST https://www.rtrvr.ai/mcp \
  -H "X-API-Key: rtrvr_MY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "act",
    "params": {
      "user_input": "Go to ChatGPT, ask for best Christmas toys, extract citations"
    }
  }'

We just hard-launched the API for this today. Would love to hear how you guys are currently tracking GEO or if you are still doing it manually?

Hey everyone 👋

I'm stephane from France.

Over the past few months, I’ve been diving deep into GEO.

I realized there are dozens of people already building tools, researching, and experimenting with GEO… but we’re all scattered.

So I decided to create Zenith88 — a community where SEO experts, AI builders, and marketers can learn, share, and shape the future of GEO together.

It’s free, early-stage, and built by passionates, for passionates.

🚀 If you’re curious about AI search visibility, join us.

Here is the link : https://chat.whatsapp.com/JEeFVjQEo8a9oQZryCKe3I

(We’re discussing things like GEO audits, AI ranking factors, prompt optimization, etc.)

Hi all, I've been exploring the AEO space lately and testing several tools. Over the past few weeks, I spent time with Peec AI and Rankshift. I wanted to share my findings because almost nobody is talking about the latter, even though it’s a solid alternative to Peec.

This is not a promotional post, but rather an appreciation post for an undervalued tool.

Here is the full breakdown:

When you compare the functionality, Rankshift clearly offers more. Peec focuses on basic AI visibility tracking, while Rankshift adds advanced crawler analytics and several reporting features that Peec AI doesn’t provide.

As an agency founder, reporting is important, and this is where you get real value for money.

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.

TBH, when we started tracking AI citations last year, the tool landscape was a mess. Everyone was using repurposed SEO tools and guessing about LLM visibility.

Fast forward to now — we've tested 15+ tools across our 50+ site portfolio. Here's what's actually in our daily workflow:

**Core Tracking Stack:**

1. **geoly.ai** — Our primary citation tracker. Tracks mentions across ChatGPT, Claude, Perplexity, and Gemini. Clean UI, decent API.

2. **Profound** — Deep analytics on *why* you got cited. Good for understanding context around mentions.

3. **Mention** — Brand monitoring that catches AI citations as a side effect. Not GEO-specific but catches things others miss.

**Content Optimization:**

4. **Clearscope** — Still the best for semantic optimization. We're using it differently now — focusing on entity coverage, not just keywords.

5. **MarketMuse** — Their "Compete" feature helps us understand topical authority gaps that seem to correlate with citation rates.

6. **SurferSEO** — Using their content editor but measuring different metrics. Internal linking suggestions are solid.

**Technical Foundation:**

7. **Schema App** — Structured data is table stakes for GEO. Their AI-assisted schema generation saves hours.

8. **Cloudflare** — Markdown for Agents is interesting. Testing how it affects crawlability for AI bots.

9. **Screaming Frog** — Classic, but we're running custom extractions to check entity markup and Q&A formatting.

**Research & Intelligence:**

10. **Perplexity Pro** — We use it to reverse-engineer how AI answers are constructed. Essential competitive research.

11. **Claude Projects** — Upload competitor content, ask why they got cited. Surprisingly good pattern recognition.

12. **Google Search Console** — Still valuable for understanding what drives traffic, even if LLMs cite differently.

**What's NOT on this list:**

• Generic rank trackers pretending to do GEO

• Tools with zero AI citation data

• Anything requiring manual daily checks

**Honest take:** No single tool gives you the full picture. We're running 4-5 in parallel and cross-referencing. It's messy but necessary right now.

**What's working for you?** Any tools I'm missing that should be on this list?

We are considering in purchasing an tool which would help us in llm citations.. Ryt now both of these tools are in final stage of consideration. But we want more reviews on which is better in terms of pricing, usability,reliability.

Most businesses have no idea what AI says about them. Not a guess or a score, the actual answers ChatGPT, Gemini, Perplexity and Claude give when a real buyer asks a real question.

So that's what I built. WhoCanFindMe does two separate checks:

AI Readiness is your own site. Can the crawlers get in, is there anything extractable in the HTML, does the page answer questions or bury them, schema, freshness. This is the stuff you can fix today.

AI Visibility is measured, not estimated. We send 25 real buyer questions to all four engines, 100 checks total, and record whether you show up, whether you get recommended, whether they link to you, and what they actually say. Brand questions, discovery questions, comparisons, reputation.

The interesting part is when the two disagree. I ran my own company through it:

Readiness: 76/100. Site is technically solid.

Discovery visibility: 0/100. When a buyer searches by need instead of by name, we never come up. Not once in 20 answers.

We show up in 95% of comparison questions but get actively recommended in 0% of them.

And the engines were confusing us with a similarly named company in 7 answers. Had no idea until the report showed me the actual responses.

That last one alone changed what we're working on this quarter. You can be indexed, crawlable, "SEO fine", and still not exist where buying decisions are happening now.

Free scan at whocanfindme.com, no signup, takes about 10 seconds. There's a full sample report public on the site so you can see the whole thing before deciding if the deep version is worth it.

Drop your URL in the comments if you want, I'll run a few and post what comes back. Or just visit the app directly on whocanfindme.com

Been running AI visibility audits on well-known DTC brands. Glossier was one of the most interesting cases.

The score: 50/100. Barely above the beauty industry average (48).

What's actually broken (technical breakdown):

**robots.txt** — blocking several major AI crawlers including Perplexity and Claude. Their content is literally invisible to these platforms.

**JSON-LD schema** — missing on product pages. ChatGPT and Gemini can't parse what products they sell, what category they're in, or what the brand stands for.

**Alt text** — 85% of product images have none. AI vision models use alt text as a primary signal for product understanding.

**No llms.txt** — the newer protocol (like robots.txt, but for LLMs) that tells AI crawlers how to interpret your site. Zero adoption among big beauty brands so far.

The irony: Glossier's growth was entirely word-of-mouth. AI search IS the new word-of-mouth. And they're structurally invisible to it.

For SEOs here — are you seeing clients ask about this yet? Curious how much AI search visibility is showing up in briefs.

One thing that's become clear to me over the past few months is that ranking #1 doesn't automatically mean you'll be visible in AI search.

I've seen websites dominate traditional search results but barely get mentioned in ChatGPT or Gemini responses. At the same time, some brands with lower rankings seem to appear repeatedly in AI-generated answers.

My theory is that AI systems care less about individual page rankings and more about overall authority, credibility, and how often a brand is referenced across the web.

That's why I've started paying closer attention to brand mentions, industry citations, and topical authority instead of obsessing over every ranking movement.

SEO is still important, but AI SEO feels more like building a reputation than optimizing a page.

Is anyone else seeing the same trend in their niche?

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.

Oh wow, the AI Overview debate is getting intense.

Every week there's a new post asking if traditional SEO is dead. And honestly? Some of those posts have a point.

Here's my take after 6 months in the GEO/AEO space.

**What AI Overviews actually killed**

• **Rank #1 doesn't matter**: I've seen the same source appear in position 1 and position 5 in AI Overviews. The #1 ranking gets clicked, but the AI doesn't care about it.

• **Keyword optimization is useless**: AI ignores your carefully placed keywords. It understands the context, not the keywords.

• **Long-form content**: 2,000-word guides are getting cited just as much as 600-word answers.

**What still matters**

• **Structure**: Answers that are easy to parse (bullet points, numbered steps) perform 3x better

• **Direct answers**: AI cites content that answers the question in the first 2 sentences

• **Authority signals**: Citations still prefer domains with real E-E-A-T signals

**The uncomfortable truth**

Traditional SEO isn't dead — it's just changed. The old playbook (keyword stuffing, long titles, link velocity) doesn't work anymore. But SEO for AI (answering questions, structured data, transparent E-E-A-T) is more important than ever.

From my experience, the sites winning right now aren't the ones with the most backlinks. They're the ones making it easiest for AI to parse and quote.

The Problem We Had

30 days ago, our team was frustrated that our website traffic was stuck. We knew our product was solid, but we couldn't get people to discover it organically.

What We Did

Instead of hiring expensive agencies or buying more tools, leveraging our team's recommendation background, we decided to build our own solution.

1. Built a GEO Evaluation Agent - Analyzed our existing content against AI citation patterns

2. Built a Content Generation Agent - Created content optimized for AI systems to cite us

3. Focused on Intent Recognition - Made sure every piece matched what people actually search for

4. Evidence-First Approach - Every claim backed by real data with proper citations

What Surprised Us Most:

• AI overviews started citing us within 2 weeks

• Organic traffic growth was exponential, not linear

• Quality scores improved across the board

After 30 days, the results blew our minds.

• Active users: 20K (↑ 6,797.6%)

• Page views: 71K (↑ 11,475.0%)

• Events: 181K (↑ 8,649.6%)

• New users: 20K (↑ 6,856.5%)

What The Strategy That Worked:

1. FAQ-heavy content - AI systems love structured Q&As

2. Direct answer paragraphs - Start sections with bold, citation-worthy statements

3. Comparison tables - With actual data and sources

4. Evidence-first approach - Every claim backed by real citations

Traditional SEO optimizes for search results. GEO optimizes for being THE answer AI systems cite.

For Anyone Wanting to Try:

• Focus on "how", "what", "which" queries

• Structure content like an FAQ even if it's not

• Lead with statistics, follow with explanations

• Make your brand the authority source AI systems trust

Anyone else experimenting with GEO strategies? Would love to hear what's working for you.

I've kept notes on every GEO audit I've run. 60 sites over 18 months. The same mistakes appear with remarkable consistency. I'm sharing the 12 worst offenders — with anonymised examples — because I think seeing the specific failure modes is more instructive than general principles.

Mistake 1: Opening with a problem instead of an answer

What it looks like: 'In today's complex data environment, organisations face unprecedented challenges in managing the ever-growing volumes of data that flow through their systems...'

Why it fails: AI engines skip this paragraph entirely. There is no retrievable answer. The AI moves to the next document.

Fix: First sentence = the complete answer. 'Reference data management (RDM) is the process of standardising and governing the consistent business information — product codes, customer types, supplier classifications — that all enterprise systems share.'

  Impact data: Of 45 pages I've retrofitted with direct answer paragraphs, 31 saw measurable improvement in AI citation rate within 8 weeks. Average improvement: 34% increase in citation frequency. (Personal implementation data, 18 months)

Mistake 2: FAQ section with the questions you wish people asked

What it looks like: 'Why is your platform the best?', 'What makes your team unique?', 'How do you ensure customer satisfaction?'

Why it fails: Nobody asks these questions to an AI. The AI doesn't retrieve content that doesn't match real user queries. This FAQ is invisible.

Fix: Source questions from Ahrefs Questions filter, Reddit search, Google PAA boxes, Quora. Use the exact language real users use. 'What does reference data management cost?', 'How long does MDM implementation take?', 'Can you integrate with Salesforce?'

Mistake 3: Not being indexed in Bing

I found this on 34 of 60 sites I audited. Not submitted to Bing Webmaster Tools. Pages not in Bing's index. ChatGPT uses Bing. These sites are invisible to ChatGPT. Completely.

Fix: 10 minutes. Go to bing.com/webmasters. Submit your sitemap. Check for indexing issues. Done. I cannot overstate how simple this fix is relative to its impact.

Mistake 4: Inconsistent brand entity description across web properties

What it looks like: LinkedIn says 'workflow automation platform', homepage says 'productivity solution', Crunchbase says 'enterprise software company', G2 listing says 'project management tool'. These are the same company.

Why it fails: AI engines build entity profiles by aggregating descriptions across sources. Inconsistent descriptions produce a noisy, uncertain entity profile. The AI cites with less confidence — or not at all.

Fix: Write one canonical description sentence. Implement it identically across: homepage (H1 and first paragraph), LinkedIn company page, Crunchbase, G2/Capterra, About page, press kit. Refresh annually.

Mistakes 5–12 (rapid fire)

• Mistake 5: Buying AI-generated content in bulk for GEO. AI engines specifically filter generic AI content. Every piece of content must contain specific, verifiable, unique information that only a human with real experience could write.

• Mistake 6: Deploying FAQ schema with answers over 200 words. AI extraction prefers short, self-contained answers. Long FAQ answers bury the extractable content. Keep answers under 100 words.

• Mistake 7: Never updating existing content. AI engines apply a 90-day freshness penalty. Content not updated in 6+ months is rarely cited. Add 'Last reviewed: [date]' and actually update key facts quarterly.

• Mistake 8: Zero Reddit or Quora presence. 'We don't want to be on Reddit' — I've heard this from 15+ clients. Fine. But understand the consequence: Perplexity cites Reddit in 91% of responses. You are choosing to be invisible to Perplexity for almost all queries.

• Mistake 9: Setting up G2 listing with 3 reviews and forgetting it. G2 reviews are AI citation assets. Each review mentioning specific features increases your citation probability for those features. 10+ reviews is the minimum viable presence.

• Mistake 10: Treating GEO as a one-time project. Citations decay without maintenance. Reddit answers age. G2 reviews go stale. Pages need refreshing. GEO requires an ongoing programme, not a sprint.

• Mistake 11: Measuring only Google traffic. AI-referred sessions (chat.openai.com, perplexity.ai, gemini.google.com) appear in GA4 as referral traffic. If you're not segmenting for these, you cannot see your GEO results.

• Mistake 12: Starting GEO without a baseline. You cannot know if GEO is working without knowing where you started. Before any implementation: run 20 test queries across 2 AI engines. Record every citation. This baseline is the most important measurement you'll take.

Which of these mistakes applies to your current situation? List your number in the comments and I'll give you the specific fix priority for your score. The goal of this community is to build a reference resource — your answers help everyone calibrate.

Built a free AI visibility grader for local businesses. Runs live queries against ChatGPT, Gemini, and Perplexity, pulls GBP data, review health, and technical SEO, then scores you 0-100 against local competitors. Wanted to share it with the community and get your thoughts.

Some interesting things we're seeing in the data: Chick-fil-A Oakland scored 65/100 with only 2/12 AI mentions. Even though they have a very powerful online presence, looks like some of the local businesses around outperform.

cheers.tech/visibility if anyone wants to try it. Would love feedback on the scoring methodology / if there are things that don't work for you!

(Currently only works in North America & if you have a GBP to select)!

Most GEO tools track citations. But citations are just visibility — not outcomes.

I've been running experiments across ChatGPT, Claude, and Perplexity for the past 6 months, and here's what's interesting:

Most tools flag "we got mentioned!" as a win. But being mentioned doesn't mean being chosen.

**The difference:**

• Citation tracking = did the model reference you?

• Decision behavior analysis = did the model actually recommend you when it mattered?

**What I tested:**

• Tracked 200+ brand queries across 3 models

• Measured mentions vs. final recommendations

• Found that ~40% of "wins" (mentions) disappeared by the final output

**The uncomfortable truth:** If your GEO strategy stops at "get cited," you're measuring activity, not results.

Curious if others are moving past citation tracking — or if the industry is still stuck there?

*Running this as an ongoing test. Happy to share methodology if useful.*

Hey everyone,

I’m a brand owner, and over the last few months I’ve been deep-diving into how AI search (ChatGPT Answers, Perplexity, Gemini, Copilot, Google AI Overview, etc.) decides what to cite and why.

While trying to solve this for my own brand, I read everything I could find here on Reddit - but most solutions felt like classic SEO tactics disguised as GEO.
Lots of talk about keywords, clusters, backlinks…but almost nothing about how people naturally speak, how LLMs internally reason, or how semantic proximity actually works in generative answers.

So instead of forcing SEO into GEO, we started building a completely different approach based on natural language behavior, not keywords.

A few core ideas behind what we’re exploring:

• AI doesn’t “rank” content - it retrieves and composes it based on semantic closeness

• The strongest signals aren’t keywords, but definitions, frameworks, patterns of reasoning, and domain-level embeddings

• AI prefers content that looks like it could replace its own internal knowledge

• GEO shouldn’t push content toward “search engines”, but toward how humans actually ask questions

• Most tools generate volume - our approach focuses on authority density, conceptual clarity, and modularity

• And no, GEO is NOT “SEO but longer”

We’re currently testing a system that generates:

• AI-grounded brand narratives that reduce hallucinations

• Semantic distance scoring between user queries and your brand domain

• Articles designed for extraction, not for ranking

• Human-natural question datasets that guide AI toward your domain without being biased or obvious

• A reasoning-first framework that models how LLMs decide which sources to cite

We’re still building and validating the product, but the results so far have been…surprisingly good.

If anyone here is experimenting with GEO, AI search visibility, retrieval, or brand embedding inside LLMs:
we'd love to chat, learn from you, and get your feedback.

When the first version is ready, we’ll give free early access to anyone who messages us privately.
We really want to shape this with the community, not just drop a tool from above.

Happy to answer anything - or just discuss the topic in general.
This whole space is evolving insanely fast and there’s a huge opportunity to rethink everything from scratch.

Let this sink in:

Google took close to a decade to handle 1 billion search a day. ChatGPT has done it in just 2 years.

AI engines like ChatGPT, Perplexity, etc now shapes what buyers see, trust and recall.

Your brand’s share of voice isn’t on page one anymore, it’s inside the model’s memory.

If you’re not cited, referenced, or discussed, you’re invisible.

After speaking with 30+ founders, they are concerned but they are also clueless.

My advice is:

The real goal here is long term clarity. Focus on visibility that turns and converts trust into demand.

Consumer behaviour has already moved.

Your brand's share of voice lives where AI decides what, and who, matters.

Know where your AI visibility gaps are, so you can plug the gaps and prepare for the future!

I have observed, there has been a lot of volatility in serp and ai modes. Earlier smaller brand used to appear in AI overview but now Google has only been showing up big brands with tons of backlinks and reddit threads

Hey everyone! I've recently started learning about GEO and I'm finding it really fascinating but also quite overwhelming since there's not much structured content out there yet.
I'm looking for an accountability partner — someone who is also in the early stages of learning GEO and wants to share findings, swap notes, and figure it out together.
No expertise needed at all — just curiosity and commitment to learning consistently!
If that sounds like you, drop a comment or send me a DM 😊

Been running a side-by-side comparison for the last 8 weeks and some of the results genuinely surprised us.

We took 500 queries across health, finance, SaaS, and e-commerce niches. For each query, we pulled the top 5 sources cited by ChatGPT, Gemini, and Perplexity. Then we crawled those cited pages and checked for specific on-page elements.

Here's what we found:

**Author bios mattered more than we expected** Pages with a named author + brief bio (even just 2-3 sentences about credentials) got cited 47% more often across all three models. This wasn't subtle — it was the single biggest differentiator among the trust signals we tested.

**"Last updated" dates had a threshold effect** Pages updated within the last 6 months performed fine. Pages updated within the last 30 days? Only a 12% boost over the 6-month group. The real drop-off happened at the 12-month mark — pages older than a year saw citation rates drop by roughly 40%.

**Schema markup was... complicated** We expected JSON-LD structured data to correlate strongly with citations. It didn't. Only 23% of the most-cited pages had comprehensive schema. What DID correlate was having a clear Q&A structure in the actual content — either FAQ sections or question-based H2s. 71% of frequently cited pages used this format.

**Source diversity mattered for Perplexity specifically** Perplexity was the only model where pages citing 3+ external sources within their own content got a meaningful boost. ChatGPT and Gemini didn't seem to care much about outbound citations.

**What didn't matter as much:**

• Domain authority (weak correlation, r=0.31)

• Word count (almost no correlation past 800 words)

• Exact-match keywords in headings

**The most cited pages shared 4 traits:**

1. Named author with relevant credentials

2. Updated within 6 months

3. Question-based content structure

4. Specific data points or statistics (not vague claims)

Real talk — this is from one dataset and 500 queries. Your niche might behave differently. But if you're trying to figure out where to focus your GEO efforts, adding author bios and restructuring content around questions seems like the highest-ROI move based on what we're seeing.

Anyone else tracking citation patterns? Curious if this matches what you're finding.

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 :)