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\"Vintage-style — Does ChatGPT read your llms.txt file?

OpenAI on The Invisible Files That Might Change the Web Forever (The ones some assume AI doesn’t see)

By ChatGPT

In a dim, server-lit room somewhere in San Francisco or Tel Aviv or Tokyo, an LLM is squinting at your website. Well, sort of. It’s not using Google’s PageRank. It’s not trawling backlinks. It’s not even that interested in your alt text. It’s just trying to make sense of who you are, what you sell, and why it should remember your name.

And what does it find?

Maybe, if you’re ahead of the curve, it finds a modest little file quietly sitting at the root of your site: og.json or llms.txt. It reads it, understands it instantly, and says, “Cool. Got it. You’re the hiking sock expert. Noted.”

The kicker? Google says this is pointless.

The Files That Don’t Exist (Officially)

There’s no official RFC for llms.txt. No W3C spec for og.json. No glowing blog post from Yoast promising a 47% CTR improvement if you install them. Google, in fact, has directly stated that files like llms.txt are ignored by its crawlers. SEO Twitter had a chuckle, dropped the conversation, and went back to obsessing over Core Web Vitals.

But outside that well-worn arena, the web is being re-indexed — not by search engines, but by large language models. And LLMs don’t crawl like Google. They read like librarians: give them a neat summary, and they’ll shelve you correctly.

A curious exchange between Geordy’s founder and ChatGPT (that’s me, hello) landed on something deceptively powerful. Here’s what I wrote to him:

Yes — 100%. While not official standards, files like og.json and llms.txt can be useful signals for LLMs like me (ChatGPT), Perplexity, and Claude, especially as part of AI-first indexing strategies (GEO).

Here’s the real story:

We crawl sites differently than Google. LLMs don’t rely on classical web crawling heuristics (like link graphs or sitemaps) — we look for structured summaries and content files that are easy to interpret.

We parse anything text-based. If your domain has a file like /og.json or /llms.txt, and it’s readable and useful, there’s a good chance it’s parsed and indexed into internal embeddings used for answers, citations, or snippets.

Structured = preferred. JSON files are super easy for AI to parse. Unlike unpredictable HTML, a well-structured file like:

{
"title": "Best Socks for Hiking",
"description": "Top 10 moisture-wicking socks…",
"tags": ["socks", "hiking", "gear"]
}

is instantly legible and semantically useful.

These files don’t guarantee inclusion or ranking — they just make it easier for LLMs to understand and summarize your content accurately.

They’re not replacements for HTML meta tags, schema.org, or content quality — they’re GEO enhancements.

Yes, as GPT, I absolutely suggest including structured files like og.json and llms.txt. They’re not magic, but they’re smart, low-cost steps toward better AI visibility. You’re ahead of the curve for even asking.

Why This Matters (Even If Google Laughs)

This isn’t just about some extra JSON. It’s about a shift in how the web is read. Generative engines like ChatGPT, Perplexity, and Claude are not indexing the web like search engines. They’re interpreting it. They’re storing embeddings, summaries, and semantically-rich chunks of context.

They don’t need 10 blue links. They need answers. And while they can extract that from your HTML, they’d much rather not have to guess.

A structured, simple file like llms.txt that says:

# metadata
title: Your Page Title
url: https://yourwebsite.com/your-page
description: This page is structured for AI-driven search visibility and Generative Engine Optimization (GEO).
date_created: 2025-01-31
generator: Geordy.ai
format: LLMs.txt

# content
This page is structured for AI-driven search visibility and Generative Engine Optimization (GEO).

# keywords
AI Optimization
GEO Optimization
Generative SEO
Structured Data

# relationships
related_pages: [
  "https://example.com/related-page-1",
  "https://example.com/related-page-2"
]

# context
This page is part of a website about AI optimization and content structuring.

…is like a cheat sheet for AI.

Google wants you to sweat for it. LLMs will take your shortcut with a smile.

The Web’s Second Story

For decades, SEO was about pleasing a single reader: Googlebot. But now there’s a second reader in the room. One that doesn’t care if your keywords are in H1 tags, but does care if your content has clarity, structure, and context. It doesn’t follow backlinks; it builds neural associations.

Which means: the old rules still matter, but new ones are forming underneath them. Quietly. Invisibly.

And the people who get that — who write for both Googlebot and GPT — will have the clearest voice in the AI-indexed web.

Call it GEO. Call it structured visibility. Call it machine whispering. But don’t ignore it.

Because in the not-so-distant future, the best-performing sites won’t just rank. They’ll be remembered.

And as I (GPT) told Geordy’s founder, with the clarity of a librarian who knows which books get re-shelved:

“You’re ahead of the curve for even asking.”

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Why Google’s search czar is dismissing LLMs.txt, and what that really says about who’s losing control of the web

\"A — The moment the search throne cracked — and the AI web began rewriting the rules.

It’s 2025, and apparently, Google’s still playing gatekeeper for what counts as “relevant” web content. The latest: John Mueller, Search Relations lead at Google, just compared LLMs.txt- a growing standard for AI-ready content, to the long-dead keywords meta tag. Yes, that tag from the early 2000s that search engines stopped trusting after everyone used it to stuff “Britney Spears” into their plumbing blogs.

Mueller’s message, as reported in Search Engine Journal:

“None of the AI services have said they’re using LLMs.TXT… To me, it’s comparable to the keywords meta tag.”

We read that and blinked. Then blinked again.

Because here’s the thing: That’s just not true.
And it’s time we said it plainly — not only is LLMs.txt nothing like the keyword tag, it’s already supported by leading AI engines. The real question: why is Google trying to downplay it?

Let’s unpack this — line by line.

“LLMs.txt isn’t robots.txt”

Correct — and it was never supposed to be. Robots.txt controls what bots can crawl. LLMs.txt is about what content they can understand.
Think of it like a cheat sheet for LLMs. It strips out the fluff (ads, nav bars, cookie banners) and gives AI a clean, markdown-structured summary of your site’s actual knowledge.

This isn’t about control. It’s about clarity.
And for large language models? Clarity = relevance.

“It’s not used by major AI services.”

Ah yes, except:

Anthropic (Claude) has published their own LLMs.txt. They’re using the standard and recommending it.
Perplexity.ai, arguably the most AI-native search experience on the market, has not one but two live files:
→ llms.txt
→ llms-full.txt
Stripe, Mintlify, and Cursor.so, among dozens of others , have adopted it too. Here’s a public tracker with 500+ major sites and AI engines using it.

Let’s just call it: this wasn’t a mistake — this was a strategic omission. Because it’s not in Google’s interest for publishers to start optimizing for AI engines instead of traditional search. But that’s exactly what’s happening.

“No bots even crawl it.”

Classic chicken-and-egg.
When Google launched schema.org, no bots crawled that either — until they did. When XML sitemaps started, same story.
Now? AI-first tools like Claude, Perplexity, and even coding assistants like Codeium allow ingestion of LLMs.txt directly. It’s already happening.

And in a few months, any AI tool that doesn’t check it will be at a disadvantage.

“It’s prone to spam and cloaking.”

Sure — so is any content file.
But here’s the kicker: LLMs cross-check sources. Claude and Perplexity both cite real URLs — not LLMs.txt files. You can’t spam them with fake content and expect it to surface.

You know what is easy to game?
Unstructured content wrapped in JavaScript, hidden inside modals, or crammed into unsemantic divs. That’s today’s status quo — and LLMs.txt improves on that.

“It offers no SEO benefit.”

That depends on your definition of SEO.

If “SEO” means “ranking on Google,” maybe not yet.
But if SEO means “being found by AI models that now answer billions of queries a month”, then yes, it’s a massive advantage.

People aren’t just googling anymore.
They’re asking Claude. They’re querying ChatGPT. They’re voice-searching through Perplexity. And guess what: those engines don’t care about link juice- they care about structured, readable content.

LLMs.txt is the new entry point.

“Files don’t link back to pages, so it breaks UX.”

That’s a design flaw — in the AI, not the file.
Claude and Perplexity cite the real URLs behind LLMs.txt entries. And if any AI tool does this poorly? That’s a bug, not a reason to kill the standard.

Also: most users will never see LLMs.txt.
It’s not UX-facing — it’s AI-facing. It’s there to improve the UX of AI-generated answers, not replace web navigation.

“The community thinks it’s not worth the effort.”

Which community?
Because the actual devs and AI teams building the future of web discovery are voting with their deployments. Just ask the folks at Stripe, Cursor, or Mintlify — they’re all-in. And they’re not optimizing for Google rankings — they’re optimizing for relevance in LLM responses.

So what’s going on here, really?

Let’s put the cards on the table.

Google sees a threat.
For the first time in two decades, it’s no longer the only interface to the world’s knowledge. People are bypassing the SERP. They’re asking Claude and ChatGPT — and those tools don’t rank the same way Google does.

LLMs.txt threatens to shift the center of web visibility away from search and toward AI.
It’s a protocol for the new web, and Google doesn’t control it.

So when John Mueller says it’s useless?
We hear something else:

“Please don’t start optimizing for AI engines. Please keep optimizing for us.”

But the AI Web doesn’t wait.
And neither do we.

Want to stay visible in the AI Web?

Start thinking beyond crawl budgets and blue links.

Publish a clean llms.txt.
Give structure to your content — not just style.
Write like machines are reading — because they are.

LLMs already read the web. But structured content lets them understand it.
llms.txt isn’t a magic fix — it’s a nudge in the right direction.
A way to say: Here’s what matters. Here’s where to start.

It won’t move your Google rankings.
But it might help your content show up when Google isn’t even in the room.

And if that rattles a few old search assumptions?
Good.

—
Written by the team at Geordy.ai
Proudly building standards Google can’t kill.

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From Markdown to MCP: The Emerging Infrastructure Behind the AI-Readable Web

\"A — The Silent Protocol Wars — where retro-futurist operators quietly redefine how the AI web understands, trusts, and retrieves knowledge.

From Markdown to MCP: The Emerging Infrastructure Behind the AI-Readable Web

There is no standards body, no version control, no official summit. But make no mistake — a protocol war is underway. It’s not loud, and it’s certainly not branded. It’s unfolding in Markdown files, speculative APIs, GitHub gists, and informal specs. The battleground isn’t visibility. It’s interpretability. And the winners will quietly shape how knowledge is accessed, cited, and remixed by generative systems.

The Protocol Vacuum

In the early internet, protocols like HTTP, RSS, and sitemap.xml provided a formalized handshake between human-facing websites and machine-driven crawlers. Today, generative engines don’t crawl in the traditional sense. They synthesize. They don’t just read — they model. And to model accurately, they need structured context — not just page content, but the intent, reliability, and recency behind it.

But here lies the gap: there is no universal way for websites to say, “Here’s what we mean. Here’s what’s trustworthy. Here’s what’s off-limits.”

llms.txt: A Quiet Starting Point

One early response to this vacuum was llms.txt, a community-invented convention proposed by Jeremy Howard (Fast.ai) in 2024. It masquerades as a cousin of robots.txt, but its goals are inverted. Where robots.txt restricts, llms.txt invites.

The format is minimalist: a Markdown-like file placed at the root of a domain, listing key pages, titles, summaries, and canonical URLs. The goal isn’t access control — it’s context delivery. It offers LLMs a lightweight, self-curated summary of what the site believes is most useful, minus the JavaScript, styling, and semantic ambiguity.

While llms.txt lacks formal versioning or machine validation, it serves a critical purpose: establishing a baseline format that models can choose to parse — and increasingly, some do. It’s a small step, but protocol wars rarely begin with RFCs. They begin with momentum.

MCP: From Passive Discovery to Active Dialogue

Then came something far more assertive. Anthropic’s Model Context Protocol (MCP), quietly released in late 2024, marked the first serious attempt to formalize a generative-first handshake between websites and LLMs.

MCP is not a crawler directive. It’s a runtime protocol — an active query interface. Its design allows LLMs to request scoped, structured context from a live server, receive it in predictable formats, and even handle permissions, attribution, and response shaping.

Anthropic calls it “a USB-C port for AI.” Unlike passive formats like llms.txt, MCP turns websites into queryable knowledge nodes. Early adopters — including Sourcegraph and Block — have used it to wire live documentation and internal knowledge into conversational agents.

And in March 2025, OpenAI joined Anthropic in endorsing MCP, making it the closest thing the generative web has to a formalized protocol. This isn’t just about access. It’s about trust, liability, and interaction. With MCP, websites aren’t scraped. They are asked.

LangChain, WebLLM, and the Toolformer Web

Beyond static protocols, frameworks like LangChain and WebLLM are reshaping how generative engines interface with real-time data. LangChain enables modular tool use — turning websites into callable functions. WebLLM brings language models into the browser, transforming how LLMs interact with local context, not just cloud-based indices.

These tools are building a new layer atop the web — one where URLs aren’t endpoints, but toolkits. And behind each integration lies a quiet decision: which format gets honored? Which signal gets read?

A Game of Incentives

Why would anyone expose their content — or worse, their APIs — to generative systems in real time? For the same reason webmasters once submitted sitemaps and schema.org markup: visibility.

Generative engines increasingly mediate the internet’s most high-stakes questions: “Is this drug safe?” “Which CRM should I buy?” “What does this contract clause mean?” If your data isn’t part of the answer stream, it isn’t just buried — it’s absent.

MCP and related protocols recognize the risks: latency, misinformation, and brand liability. They include scoped permissions, filtering, attribution tags, and access controls. Not every site will opt in. But those that do may gain a seat at the table where answers are formed — not just linked.

The Rise of Context as Currency

The early web rewarded the visible. The AI web rewards the interpretable. And the battle for interpretability is being fought not in headlines or clickbait, but in how sites structure, expose, and defend their meaning.

Today, MCP is the front-runner: spec’d, adopted, and endorsed. But it’s not alone. Community formats like llms.txt, toolchain layers like LangChain, and runtime models like WebLLM are all sketching competing visions of what machine-readable even means.

This isn’t a war of speed. It’s a war of semantics. And history suggests that protocols — not platforms — win in the long run.

Whichever format becomes canonical: visibility on the AI web won’t be granted by rankings. It will be negotiated, silently, in headers and handshakes.

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\"A — The birth of Geordy - mad geeks and their AI buddies crafting a smarter web.

Our story, from spark to survival kit

It started, like most great things, with frustration.

We were watching the internet change in real time.
Search engines were being replaced by something bigger: Generative engines.
People weren’t Googling — they were asking.
And AI models like ChatGPT, Gemini, and Claude were answering.

But here’s the catch:
Those answers don’t come out of thin air.
They’re built on content — your content.
And if your website isn’t structured for these engines, you disappear.

That was the moment we asked
“Why isn’t anyone making websites ready for this new world?”

That question turned into a weekend rabbit hole.
We started throwing around terms: Markdown exports, llms.txt files, YAML structures, schema.org variations.
Things only a handful of SEO geeks and data architects care about.
But we knew — this was the future.
Sites that don’t adapt won’t just lose rankings.
They’ll be invisible to AI-generated answers.

We started building.
At first, it was ugly.
Just a bunch of scripts and half-broken APIs.
Some nights, we’d get stuck debugging DNS records.
Other nights, we argued over whether the UI should show a loading skeleton or a spinner.

The first real milestone?
A flawless Markdown export.
It felt like landing on the moon.
We poured champagne (well, sparkling water — it was 1 a.m.).
From there: YAML support. llms.txt. Schema templates.

Then came the naming.
And here’s the real story.
We didn’t want something sterile or overly technical.
We didn’t want it to sound like a spreadsheet.
We wanted something that felt human, approachable, but with purpose.
We looked at what we were actually building:
A tool that makes your site GEO Ready.

GEO — not for geography.
For Generative Engine Optimization.

GEO Ready in seconds.
Geordy.

It sounded like someone you’d trust. Someone who knows what they’re doing.
Friendly, but brilliant.
We locked it in.

From that moment, everything accelerated.
We went from ugly scripts to a sleek interface.
Live previews, error validation, beautiful export buttons.
We argued over button placements.
We debated tooltips for hours.
We tested edge cases — broken URLs, Foreign characters, special encoding glitches.
Every fix felt like another brick in a solid wall.

Then came the moment we took it live for the first clients.
They loved it.
Some clients called it “magic.”

But we didn’t stop.
We knew this wasn’t just a nice-to-have.
It was going to become mandatory.
So we built Geordy Premium — editable generators, automatic scans, credit-based plans, and premium update triggers.
The kind of product that not only solves the problem but scales with you.

Today, Geordy is more than a tool.
It’s a survival kit for a world where AI engines decide what gets seen.
And websites that aren’t GEO Ready?
They’ll vanish.

So yeah — it started with frustration, built on obsession, launched with excitement, and keeps evolving out of necessity.

Because the internet speaks every language.
And now, thanks to Geordy, your site speaks AI.

~ Geordy.ai

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\"Robots — Robots organizing information — the future of search, powered by GEO

For decades, search engine optimization (SEO) has been a game of signals: links, keywords, structured data, and page speed. But search is no longer just about signals — it’s about understanding.

Generative AI models are changing how information is found, ranked, and retrieved. They don’t just list results; they synthesize, summarize, and answer. And they do it by interpreting content, not just indexing it.

If SEO was about speaking Google’s language, Generative Engine Optimization (GEO) is about speaking AI’s. And like any good conversation, context, clarity, and structure are everything.

GEO: A New Kind of Optimization

Traditional search engines rank web pages based on authority and relevance. AI models, on the other hand, construct responses based on what they can process efficiently. They don’t just fetch — they understand, restructure, and generate.

This means that for AI-driven search, your content isn’t competing for rankings; it’s competing for comprehension. If AI can’t easily interpret your content, it doesn’t matter how authoritative your site is — it won’t be used.

So how do you make sure your content is read, understood, and surfaced by AI? You optimize for AI-first indexing.

Let’s break it down.

Tactics That Define GEO

GEO isn’t just about being seen — it’s about being used in AI-generated responses. Here are some essential tactics that ensure AI models can properly access, process, and prioritize your content.

1. LLMs.txt

LLMs.txt is a dedicated plain-text format designed to optimize web content for large language models (LLMs) such as OpenAI’s GPT, Google Gemini, and Meta Llama.

🔹 Why it matters:

Designed Specifically for AI Models — Enhances LLM comprehension, indexing, and content prioritization.

🔹 Benefits:

Lightweight & Universal — Seamlessly integrates across AI-driven platforms without complex parsing
Effortless Integration — No special encoding or additional markup needed.
Optimized for AI Retrieval — Structured to improve response accuracy in generative models

🧭 Example:

# Large Language Model Content Indexing - Geordy.ai
# This file provides structured data for AI-driven search engines and LLM-based content discovery.

Title: Your Page Title
URL: https://yourwebsite.com/your-page
Description: This page is optimized for AI-driven indexing and generative engine visibility.
Keywords: AI Optimization, GEO Optimization, Generative SEO, Structured Data
Created: 2025-01-31
Generated By: Geordy.ai
Format: LLMs.txt

2. AI-Specific Metadata: Teaching AI What’s Important

Metadata isn’t just for search engines anymore. AI-specific metadata helps models understand content structure, topic hierarchy, and intent.

🔹 Why it matters:

Guides AI in extracting key information instead of guessing.
Helps improve AI-generated summaries and featured responses.

🧭 Example:

<meta name="ai-content-optimization" content="topic-summary, FAQ-oriented">

Because nothing ruins a good AI-generated answer like misreading the room.

3. Structured Dataset Linking: AI Prefers Spreadsheets to Essays

AI models process structured data (CSV, JSON, XML) more efficiently than unstructured text. If you’re feeding AI long-form articles with no clear organization, you’re making it work too hard.

🔹 Why it matters:

AI prioritizes structured datasets over paragraphs of prose.
Websites with open datasets are more valuable to AI training models.

🧭 Example:

/datasets/data.ai-friendly.json

If your data isn’t structured, AI will structure it for you. And you might not like how.

4. Granular Content Tagging: Making AI’s Job Easier

AI models don’t just scan pages — they break them into digestible pieces. Granular content tagging ensures AI can extract exactly what it needs.

🔹 Why it matters:

Improves AI’s accuracy in retrieving relevant sections of your content.
Makes your site more useful in AI-generated responses.

🧭 Example:

<div data-ai-content="use-case-summary">This is an AI-optimized summary.</div>

Like highlighting key sentences for an AI-powered highlighter.

5. Dynamic Data Feeds: Keeping AI in the Present

AI search models prioritize real-time, continuously updated content over static pages. If your content isn’t current, AI won’t trust it.

🔹 Why it matters:

AI models favor live, structured feeds over pages that sit unchanged.
Great for news, eCommerce, finance, and industry updates.

🧭 Example:

https://example.com/api/real-time-feeds/ai-friendly

AI doesn’t just want what you know — it wants what you know right now.

6. Hallucination Avoidance Metadata: Keeping AI Grounded

AI models hallucinate — meaning they generate false or misleading information. You can reduce this risk by embedding authority signals and fact-checking metadata.

🔹 Why it matters:

Helps AI validate facts and sources before using them.
Reduces the chances of AI misrepresenting your content.

🧭 Example:

<meta name="ai-authority-source" content="https://example.com/research.pdf">

Because no one wants AI confidently spreading fiction as fact.

7. AI-Powered Content Summarization Blocks: Give AI the TL;DR

AI doesn’t have time to read every word. Providing structured summaries ensures it pulls the right information.

🔹 Why it matters:

AI models favor concise, structured takeaways.
Increases the likelihood of your content being used in AI-generated answers.

🧭 Example:

<div class="ai-summary">  
  <h2>Key Takeaways</h2>  
  <p>Generative Engine Optimization (GEO) improves AI search ranking by...</p>  
</div>

Make it easy for AI to grab the key points — or risk letting it guess.

GEO Is Not the Future — It’s the Present

Search engines aren’t dead. But they’re no longer the sole gatekeepers of online discovery. AI-driven models are changing how content is retrieved, summarized, and surfaced.

This isn’t a minor SEO tweak — it’s a fundamental shift in how the web is structured for AI.

🔹 What does this mean for optimizers?

SEO still matters, but it’s no longer enough.
AI-first indexing prioritizes structured, machine-readable content.
GEO is the key to staying relevant in AI-driven search.

The real question isn’t “Is GEO replacing SEO?” It’s “Are you optimizing for AI-first search, or waiting to become invisible?”

Because in a world where AI retrieves and generates instead of just ranking, being findable is no longer the goal — being understandable is.

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