Do You Need an llms.txt File for Better GEO & SEO? Discovery vs. Functionality in the AI Era
Lately, the SEO community has been debating a new technical trend: the adoption of llms.txt files and parallel Markdown directories.
The discussion reached a boiling point when SEOs noticed that Google had quietly implemented llms.txt files and simplified Markdown versions of pages across its official developer portals (developers.google.com).
This move raised immediate questions. For years, Google representatives have maintained that specialized, machine-readable alternative pages are not necessary for standard search performance. Why, then, is Google using them on its own properties? Is there a hidden ranking benefit? Should you start converting your website’s content into raw text files to prepare for an AI-driven web?
To help you cut through the noise, we looked at the public responses from Google Search Advocate John Mueller. In this guide, we will break down the differences between optimizing for search engine discovery versus user-side functionality, examine the token economics of AI-driven traffic, and explain why you should prioritize immediate business needs over speculative tech trends.
What Are llms.txt and Machine-Readable Markdown Files?
Before diving into the strategy, let’s define the terms.
- llms.txt: A proposed standard format. It is a simple text file placed in the root directory of a website (similar to robots.txt). It provides brief, clean directions and a map of high-priority resources specifically formatted for Large Language Models (LLMs) and AI agents.
- Parallel Markdown Pages: Simplified, text-only versions of standard HTML pages written in Markdown syntax. They strip away layout assets, CSS, tracking scripts, and navigation menus to deliver raw, structured content.
Google’s Stance: It’s Not an SEO Play
When webmasters noticed Google’s developer portal using these formats, some assumed it was a signal that Google’s core search algorithm now favors websites that offer machine-digestible files.
However, in a public discussion thread on X (formerly Twitter), Google Search Advocate John Mueller clarified that this implementation has nothing to do with organic search rankings or crawler optimization.
“The short answer is that it’s not done for search,” Mueller stated on X. “There’s more to websites than just SEO :-).”
This is an important reminder for digital marketers: not every technical change on a website is designed to appease search crawlers. Websites are complex digital assets that serve multiple technical, functional, and user-experience requirements.
In this case, Google’s web teams implemented these files to address an entirely different workflow: developers using autonomous AI tools directly on the site.
The Concept of the “Dual Web”: Discovery vs. Functionality
To understand why a brand would publish machine-readable formats if they offer no ranking boost, you need to separate your digital strategy into two distinct operational layers: discovery and functionality.
Addressing the topic in a follow-up post on his X thread, Mueller explained:
“The longer & nuanced version is that it’s worth separating ‘discovery’ (finding the website or pages with a global search engine) vs ‘functionality’ (there’s probably a more accurate term for this, but basically: once someone has found the page, helping them to best do the task they want to do).”
Let’s look at how these two layers differ:
| Feature | Discovery Layer | Functionality Layer |
|---|---|---|
| Primary Goal | Getting indexed and ranked; driving organic traffic. | Helping users complete on-site tasks efficiently. |
| Target Audience | Search engine crawlers (Googlebot, Bingbot) and searching users. | On-site visitors, IDE extensions, and user-delegated AI agents. |
| Key Technologies | HTML, Schema.org structured data, XML Sitemaps, robots.txt. | Clean UI/UX, clear Calls to Action (CTAs), API endpoints, Markdown. |
| SEO Impact | Direct (influences organic visibility and CTR). | Indirect (influences on-site engagement, retention, and conversion). |
The Call to Action (CTA) Analogy
To put this into perspective, think about standard conversion rate optimization (CRO). Writing on X, Mueller compared machine-readable files to traditional Call-to-Action (CTA) elements:
“Perhaps that’s similar to CTA’s on traditional pages? You don’t ‘do them’ for SEO (to be found), but if you’re responsible for the website overall, ensuring a high ‘discovery rate’ (SEO) together with a high conversion rate is useful to justify your work,” Mueller noted.
You don’t place a “Book a Demo” button on a page to improve your Google ranking. You do it to help the user complete their goal once they arrive.
Similarly, providing an AI-friendly text file is a post-discovery feature. It is there to help an AI agent—acting on behalf of an on-site user—read and process your content cleanly.
The Economics of the Developer Web: Saving Tokens
If llms.txt files do not help with search discovery, why do developer portals use them? The answer comes down to the workflow of modern software engineering and the cost of processing data in generative AI.
Today, software developers rely heavily on AI coding assistants (like GitHub Copilot, Cursor, or custom IDE integrations). These tools often retrieve documentation directly from web pages to generate code recommendations.
However, standard web pages are highly complex. They are filled with styling code, tracking tags, sidebar widgets, and navigation headers. While a human browser effortlessly ignores these visual elements, an LLM must process every character.
Processing excess code incurs a real cost, measured in tokens.
Traditional HTML Page
[Header Code] -> [Nav Menu Code] -> [API Article Text] -> [Sidebar Ads] -> [Footer Scripts]
* Result: High token count, slower processing, increased API cost.
Machine-Readable Markdown
# API Article Text
* Result: Low token count, near-instant processing, minimal API cost.
By offering a clean, pre-parsed Markdown alternative, sites hosting technical documentation help AI-driven tools extract exact context without wasting processing power.
Continuing his thread on X, Mueller wrote:
“To get back to the developers.google.com site, AI coding has gotten very popular, and these coding systems can be (I think) efficient and accurate with the code they produce if they can easily read / parse reference material, such as developer documentation.”
He added in a subsequent post:
“In those cases, it can help to give them a way to understand the context of the documentation they’re looking at, as well as a simplified version of the reference page (eg, in markdown). OF COURSE they can read HTML just fine, so this is imo more of a temporary crutch, perhaps to save some tokens.”
As language models become more efficient at parsing raw HTML and token costs continue to drop, the need for these alternative text versions will likely decrease. For now, however, they serve as a useful utility for developer-focused platforms.
Why Non-Technical Sites Should Avoid Markdown Formats
It is common in the SEO space to see a technical solution designed for a specific niche (like developer APIs) and attempt to apply it universally. However, creating text-only directories for standard commercial, informational, or e-commerce sites is often unnecessary—and can even be counterproductive.
Mueller cautioned in his post on X:
“For non-developer sites, I don’t think this makes much sense, even with more agentic traffic in the future (and if you check your logs, you’re not getting a lot of that at the moment). Making a markdown version of a shoe’s specs is not going to get you more sales (competitors appreciate it tho).”
Here are two primary reasons why non-developer sites should avoid publishing simplified Markdown versions of their pages:
1. It Completely Bypasses the Brand Experience
If you run an e-commerce site, you convert visitors through high-quality photography, structured user reviews, interactive product comparisons, and persuasive copy. Delivering a raw Markdown file with a product’s dimensions bypasses the exact visual and emotional cues that drive human purchase decisions.
2. It Lowers the Barrier for Competitor Scraping
Web scraping is a significant concern for e-commerce brands. Competitors use automated scrapers to monitor your pricing, inventory levels, and product descriptions to undercut your business.
If you publish clean, unformatted, easily parsed Markdown files of your entire product catalog, you are removing all technical friction for competitor bots. You make it incredibly easy for rivals to scrape your proprietary data and replicate your listings.
Prioritizing Your SEO Strategy: Needs Before Dreams
With so much discussion surrounding the “future of search” and how AI agents will navigate the web, it is easy to lose focus on the fundamentals. Many brands are tempted to divert resources toward preparing for a hypothetical future where AI bots make all purchasing decisions, neglecting their current web performance in the process.
In the final post of his thread, Mueller advised webmasters to keep their engineering resources focused on what drives revenue today.
“And (I know, nobody reads this far), if you think this is important to prepare for when agents are everywhere: your site (all sites) have much more important things to do for SEO than to prepare for a potential future situation that may or may not come. Prioritize needs before dreams.”
To help you structure your workflow, use this prioritization framework based on Mueller’s advice:
- Publishing high-quality, expert-led content (E-E-A-T)
- Improving Page Experience (Core Web Vitals, Mobile UX)
- Implementing standard Schema.org structured data
- Optimizing internal link equity and crawl paths
- Creating custom text files for experimental AI scrapers
- Stripping down consumer pages into raw Markdown
- Optimizing specifically for hypothetical AI agent buy-flows
The Immediate Action Items: Focus on the “Needs”
Instead of building speculative formats, focus on established optimizations that benefit both human searchers and search engines today:
- Optimize Your Core Web Vitals: Ensure your pages load quickly, run smoothly, and maintain visual stability.
- Use Structured Data (Schema.org): This is the standardized, industry-wide method for helping search engines understand your content. Schema markup helps you earn rich results (like review stars, FAQ dropdowns, and product prices) on search engine results pages.
- Address Crawl Budget and Technical Errors: Use Google Search Console to identify and fix 404 errors, crawl loops, and unindexed pages.
- Create Helpful, User-First Content: Ensure your content directly answers the queries your target audience is searching for, backed by real-world expertise.
