22 MIN READDecember 24, 2025
Is llms.txt Useful for E-commerce? The Reality Check
Myths, Misconceptions, and What Actually Matters for E-commerce
Myths, Misconceptions, and What Actually Matters for E-commerce
llms.txt won't help your store. At least not the way you've been told. Not with a plugin. Not with an agency promising "Generative Engine Optimization." Not by following the advice flooding your LinkedIn feed.
I know that's a strange thing to hear from someone who actually offers AI optimization for e-commerce stores. Stay with me. We get there.
I've been tracking llms.txt since January '25, when it was still a niche proposal most marketers hadn't heard of. But four months ago, I decided to find and read everything. 73 articles, guides, podcasts, and YouTube videos. Posts from Rand Fishkin. Deep dives from the Ahrefs team. Tutorials from Vercel. LinkedIn threads from people who definitely have opinions.
I found 100+ different claims about what this file does. From 73 pieces. The math doesn't work, but somehow the industry made it work.
Some say llms.txt is the future of AI visibility. Some say it's useless. Some say Google ignores it. Some say ChatGPT already fetches it. Plugin developers want you to generate it automatically. SEO agencies want to sell you packages built around it.
If you're confused, you should be. The information out there is a mess.
But here's why I'm telling you llms.txt won't help: because the way most people are implementing it, it can't. The file isn't magic. The plugins are garbage(We will get back to this). The promises don't match the data. And until you understand what llms.txt actually is, versus what people are selling it as, every decision you make about it will be wrong.
Hold tight. This is going to be a long one.
Three claims keep circulating. You've probably heard at least one. Let's put them down.
The most common pitch is that llms.txt improves your visibility in AI answers. Add the file, get cited more often.
SE Ranking tested this. They analyzed 300,000 domains looking for correlation between llms.txt presence and AI citation frequency. They found nothing. Zero correlation. When they removed llms.txt from their predictive models, the models actually got more accurate. The file was adding noise, not signal.
Google confirmed it from their side. A representative stated that to appear in AI Overviews, you should "simply use normal SEO practices." llms.txt won't be a factor.
This doesn't mean the file is useless. It means the file doesn't do what most people think it does. Adding llms.txt to your store won't make Gemini cite you more often. It won't make ChatGPT recommend your products over competitors.
If your product data is broken, wrapping it in a Markdown file just gives AI a cleaner view of broken data.
That's the visibility myth. The security myth is even stranger.
People treat llms.txt like a security gate. They think it controls what AI can access.
It doesn't. The names sound similar, but the functions are opposite.
robots.txt is a gatekeeper. It tells crawlers where they cannot go. Restrictive. Crawlers that respect it will stay out of blocked paths.
llms.txt is a tour guide. It tells AI what to read first. It's not a rule. It's a suggestion. It doesn't block AI scrapers from accessing your content. It doesn't protect anything. It simply offers a cleaner version of information that's already public.
If you're worried about AI training on your content, llms.txt won't help. You need robots.txt rules targeting specific AI crawlers. Different problem, different solution.
Now for the myth that kills the conversation before it starts.
Google's John Mueller has been blunt. On Bluesky, he wrote: "FWIW no AI system currently uses llms.txt."
He doubled down in a follow-up: "It's super-obvious if you look at your server logs. The consumer LLMs / chatbots will fetch your pages, but none of them fetch the llms.txt file. Maybe they will tomorrow? Maybe I'll win in the lottery tomorrow?"
This leads people to a logical but incomplete conclusion: if Google ignores it, why bother? Right? ...Right??
Two problems with that logic.
First, llms.txt was never built for Google Search. The spec is explicit. It's designed for inference, not indexing. When someone asks a coding assistant about your documentation, the assistant fetches your llms.txt to understand your site structure. That's the use case. Not search rankings. Not Google's index.
Second, Mueller's claim doesn't hold up against the data. Profound, a platform that tracks LLM-website interactions, published a study in June 2025 showing that ChatGPT is actively visiting these files. Their analysis found llms-full.txt files receiving a median of 79 visits over seven days, with ChatGPT accounting for the majority. Someone at OpenAI is fetching these files, whatever Google's position might be.
But here's the part that should make you pause.
Google itself uses llms.txt. Visit ai.google.dev/api/llms.txt and you'll find a properly structured file for the Gemini API documentation. The Search team calls it useless. The Gemini team built one anyway.
That contradiction tells you everything.
The people dismissing
llms.txtare thinking about traditional search. The people building with it are thinking about AI agents and developer tools.
Different problems. Different tools. And that's exactly why the confusion exists.
Confusion doesn't spread itself. It needs help. Four groups, as far as I can count, each pulling in different directions.
High-profile SEO voices dismissed llms.txt the moment Google rejected it. Their logic is simple: "If it doesn't affect rankings, it doesn't matter."
This makes sense if your entire worldview is Google. But it ignores that llms.txt was never designed for search engines. Dismissing a screwdriver because it makes a bad hammer isn't insight. It's using the wrong tool and blaming the manufacturer.
The skeptics aren't wrong that llms.txt won't boost your Google rankings. They're wrong to conclude it's therefore worthless. They're reviewing a bicycle and complaining it doesn't fly.
On the opposite end, agencies and consultants are selling llms.txt as the next frontier of "Generative Engine Optimization." They imply that adding this file will make AI recommend you. They promise visibility gains they cannot prove.
The SE Ranking study found zero correlation between llms.txt and AI citations. Zero. Yet the pitch continues: "Get ahead of your competitors. Future-proof your AI presence. Don't get left behind."
This is the SEO equivalent of selling flood insurance in the Sahara. Maybe the flood comes someday. But probably not how they're describing it.
Store owners implement the file expecting ChatGPT to start sending customers. When nothing happens, they conclude the whole thing is a scam. The problem wasn't llms.txt. The problem was buying a lottery ticket and calling it a retirement plan.
WordPress plugins now offer one-click llms.txt generation. Flip a switch, get a file. Optimization complete. Go home early.
Too easy. Suspiciously easy.
These generators crawl your site and produce a list of links. That's it. No curation. No context. No understanding of what actually matters to your business. The output is a glorified sitemap wearing a Markdown costume.
The SEO plugin that powers nearly half the internet is rolling this out to millions of WordPress sites. The stated goal is to help AI understand your content better. The unintended consequence is millions of auto-generated files that teach AI nothing useful. That's not adoption. That's pollution.
Auto-generated llms.txt without clean underlying data is just automation of garbage. The file isn't magic. If your product descriptions read like someone had a stroke on the keyboard, the generator will faithfully reproduce that chaos in Markdown format. Now your mess is machine-readable. Congratulations.
Some developers are building llms.txt files incorrectly, then blaming the standard when it doesn't work. This is like assembling IKEA furniture without the instructions and leaving a one-star review because your bookshelf has three legs.
Here's what keeps going wrong:
Wrong format. The spec requires Markdown. Many files are served wrapped in HTML or with incorrect content types. AI agents expect clean text. When they get HTML soup, they choke on your angle brackets.
Wrong location. The file must live at the root domain. Some sites bury it in subdirectories, hidden like a teenager's diary. No agent will find it there.
Bloated files. The company that keeps a quarter of the internet online built an llms-full.txt file running approximately 3.7 million tokens. Most AI context windows tap out around 200,000. That file is technically correct the way a dictionary is technically a novel. All the words are there. None of the usefulness.
These implementation failures create a feedback loop. Developers build it wrong, see no results, write blog posts about how llms.txt is useless. Others read those posts and skip implementation entirely. The standard gets blamed for user error. The circle of misinformation continues.
And all four groups feed each other. Skeptics dismiss it, hype men overclaim it, plugin developers automate garbage, bad implementers break it. Everyone points at the standard instead of looking in the mirror.
Strip away the myths. Ignore the hype men. Forget the plugins. Here's what the file actually does.
When an AI agent visits your website, it faces a problem. Your beautifully designed pages are a nightmare to parse. Navigation menus, tracking scripts, cookie banners, related product carousels, newsletter popups, footer links, social icons. A 2MB page might contain 2KB of information that actually matters. The rest is noise.
The agent has a context window. Think of it as working memory. Every token it spends parsing your navigation is a token it can't spend understanding your products.
It's like asking someone to read your résumé while you blast an air horn in their face. They might get the gist. They'll miss the details.
llms.txt solves this by offering a clean alternative. A Markdown file at your root domain that says: "Here's what this site is about. Here's what matters. Here are the links to the good stuff."
It's a "read this first" sign. Not a "do not enter" sign. Not a ranking signal. Not a magic visibility boost. Just a shortcut to clarity.
On September 3, 2024, Jeremy Howard published a proposal. Howard founded Answer.AI and co-founded Fast.ai. He's a prolific entrepreneur and seemingly pioneering AI adoption.
His problem was specific. When coding assistants like Cursor try to read documentation, they waste tokens on navigation menus, tracking scripts, and HTML noise. His solution was simple: a Markdown file at the root of your website. Clean text. No noise. A map that tells AI where to find what matters.
That's what llms.txt was built for. Helping AI read documentation at inference time. Not training. Not search rankings. Just cleaner answers when someone asks a coding question.
The spec mentions e-commerce in passing. It lists online stores alongside personal CVs and university course catalogs. Possible use cases. Not the primary purpose.
But somewhere between September 2024 and now, the message got scrambled. The developer tool became a marketing buzzword.
We've been tracking this since the beginning. The spec is simple. The implementations are where things go wrong.
The official spec keeps it minimal. Here's what's required:
An H1 header with your site name. That's it. That's the only mandatory element.
Everything else is recommended, not required:
The format is Markdown. Human-readable and machine-parseable. No special encoding. No complex syntax. Just clean text with some structure.
Here's what a basic version looks like:
# Your Store Name
> Brief description of what you sell and why it matters.
## Products
- [Winter Boots Collection](https://store.com/products/winter-boots): Insulated footwear rated to -30°C, sizing charts included
- [Running Shoes](https://store.com/products/running-shoes): Performance footwear with durability specs
## Policies
- [Returns & Exchanges](https://store.com/policies/returns): 30-day return window, condition requirements
- [Shipping Information](https://store.com/policies/shipping): Delivery timeframes by region
## Optional
- [About Us](https://store.com/about): Company history and valuesNotice something? The descriptions aren't just repeating the link titles. They're telling AI what it will find. Context before the click. That's what generators miss entirely.
The spec defines two approaches. Mixing them up causes problems.
llms.txt | llms-full.txt | |
|---|---|---|
| Purpose | Table of contents | Complete library |
| Size | Small, under 10KB | Large, potentially megabytes |
| Contains | Links with descriptions | Actual page content, concatenated |
| AI behavior | Reads this first, then fetches pages | Reads everything in one request |
| Best for | Site overview, navigation | Deep context without multiple fetches |
The Profound study found that llms-full.txt files receive significantly more AI visits. Median of 79 visits versus 14 for standard llms.txt. AI agents prefer the complete library when they can get it. Less fetching, more reading.
This makes sense. If you're an AI trying to answer a question about a product, would you rather follow five links and parse five pages, or read one file that contains everything? The answer is obvious. The lazier path is often the smarter path.
Here's something most articles won't tell you. The spec's creators don't just use llms.txt and llms-full.txt. Look at FastHTML's implementation and you'll find llms-ctx.txt and llms-ctx-full.txt. The "ctx" stands for context. They created purpose-specific files for their documentation context.
This matters because it reveals the spec's actual intent: create files that match your use case. The naming tells AI what it's getting. llms-ctx.txt says "this is context information." The format stays the same. The rules stay the same. The name communicates purpose.
For e-commerce, this insight changes everything.
Your main llms.txt should describe your store. Who you are, what you sell, your policies, your value proposition. But your product catalog? That's a different beast entirely. Thousands of SKUs, hundreds of variants, prices that change weekly. Cramming all of that into your main llms.txt is like stapling your entire inventory list to the front door. Technically possible. Practically useless. You'd overwhelm any AI trying to understand what your store is actually about.
That's why we create llms-products.txt and llms-products-full.txt for our e-commerce clients.
Think of it like a shopping mall. Your main llms.txt is the building directory in the lobby. It shows what stores are here and where to find them. llms-products.txt is the product catalog inside your specific store. You don't tape your entire inventory to the mall directory. You put a clear sign that says "products this way" and let AI find what it needs when it needs it.
The files follow the exact same spec. Same Markdown format. Same structure rules. Same everything. AI crawlers will find them because the naming is obvious. "llms-products" tells any AI tool exactly where to go for product information. Clean separation. Clear purpose. No bloat in your main file.
This isn't us inventing something new. It's us applying the spec's own logic to e-commerce's specific challenges. The FastHTML team did it for documentation context. We do it for product catalogs.
The flexibility of the format is a feature. API documentation sites organize by endpoints. E-commerce stores organize by product categories. Service businesses organize by offerings. The structure should match your business, not some arbitrary template.
But that flexibility is also why auto-generators fail. They don't know what matters to your business. Only you do. A generator sees URLs. It doesn't see strategy.
Your returns policy might be critical for AI to understand before recommending your products. A generator doesn't know that. Your sizing guide might be the difference between a confident AI recommendation and a hallucinated guess. A generator gives it the same weight as your cookie policy.
The file is simple. The decisions about what goes in it are not.
And that's exactly why most implementations fail. Not because the standard is broken. Because the thinking behind the implementation is missing.
llms.txt Now. Why Can't I Just Use That?"You can. We use generators ourselves. They're useful tools.
The problem isn't the generator. The problem is what happens after.
A generator crawls your site, lists your pages, and outputs Markdown. If your data is clean, you get a clean file. If your data is a mess, you get a beautifully formatted mess.
Here's the difference:
What a generator outputs from messy data:
- [Winter Boots](https://store.com/products/winter-boots): Winter Boots product pageWhat a generator outputs from clean data:
- [Winter Boots Collection](https://store.com/products/winter-boots): Insulated footwear rated to -30°C, sizing charts, waterproofing specs. Updated weekly.The first tells AI nothing it couldn't guess from the URL. The second tells AI exactly what it's getting. Same generator. Different input. Completely different usefulness.
Generators don't fix your data. They document it. If your product descriptions contradict each other, your category structure makes no sense, and your pricing lives in seventeen different formats, the generator will faithfully reproduce all of that in Markdown.
The file isn't the problem. The file is a mirror.
Even with clean data, generators miss things that require judgment.
Your returns policy might be critical for AI to understand before recommending your products. A generator doesn't know that. Your sizing guide might be the difference between a confident recommendation and a hallucinated guess. A generator gives it the same weight as your cookie policy.
Product relationships matter. This laptop works with these accessories. This camera requires these lenses. A generator sees individual URLs. It doesn't see connections.
Curation requires understanding your business. Generators see structure. They don't see strategy.
The real danger isn't using a generator. It's using one and walking away. And belive me. It's very easy to set things up and forget about them.
Your store changes. New products launch. Old ones retire. Prices shift. Campaigns rotate. A generator that ran six months ago has no idea your bestseller is discontinued. It's still pointing AI to dead pages at outdated prices.
When AI confidently tells a customer your winter boots are $89 and they're actually $129, that's not a hallucination. That's your stale file doing exactly what you told it to do. Months ago.
Static automation for dynamic businesses creates misinformation.
And guess which business has the most chaotic, the most dynamic, and the most "oh shit! here we go again" nature?
I leave the answer to you.
Here's what happens when your data contradicts itself.
Your product page says the boots are waterproof. Your description says water-resistant. Your specs table says "splash-proof up to 30 minutes." Three claims. One product.
A human might figure out what you mean. AI doesn't guess charitably. It sees three conflicting statements and picks one. Randomly. Whatever weighting the model applies that day.
The more contradictions in your data, the more coin flips you're asking AI to make. Every flip is a chance for wrong information. Wrong price. Wrong feature. Wrong product entirely.
Generators don't create these contradictions. They don't resolve them either. They just make them accessible. Now AI can confidently misrepresent your products at scale.
Generators are a starting point, not a finish line.
Run the generator. Audit the output. Fix what's broken in your actual data. Curate what matters. Then maintain it as your store evolves.
That work benefits your store more than any txt file ever will. It benefits everything. From SEO to UX to AI recommendations.
Fair question. Let's be specific.
Nobody can promise that llms.txt will make ChatGPT recommend your products tomorrow. The data doesn't support that claim. Anyone selling that outcome is selling dreams.
But implementing llms.txt properly has no downside. None. It doesn't hurt your SEO. It doesn't confuse search engines. It doesn't break anything. The worst case is nothing happens.
The best case? You're positioned ahead of every competitor when AI agents start relying on these files consistently. And one way or another, they will. ChatGPT is already fetching them. The Gemini team built one for their own documentation while their Search colleagues were publicly dismissing the standard.
AI takes the path of least resistance. When your store offers clean, structured data, you become the easy choice. Your competitor's cluttered, inconsistent pages become the hard choice. AI doesn't care about brand loyalty. It cares about efficiency.
Be the efficient option.
Here's what nobody talks about: building llms.txt properly forces you to fix things you've been ignoring or didn't even know were broken.
You can't create a clean index of your products if your product data is a mess. You can't point AI to your category pages if your category structure makes no sense. You can't describe your pricing clearly if your pricing is scattered across inconsistent formats.
The
llms.txtfile is the output. The real value is everything you fix along the way.
When we build llms.txt for e-commerce stores, we find problems. Always. Category pages with broken schema. Product descriptions that contradict each other. Pricing displayed three different ways on three different templates. Inventory data that doesn't match reality. Policies buried in PDFs that no crawler can read. XML product feeds that don't reflect store data.
These problems existed before we arrived. They were hurting your SEO. They were confusing your customers. They were costing you money in ways you couldn't measure.
The llms.txt project becomes the reason to finally fix them. And once they're fixed, every crawler benefits. Googlebot reads your pages more accurately. Bing understands your products better. AI agents get cleaner data. Your customers find what they're looking for.
You came for AI optimization. You leave with a healthier store.
Some stores are waiting. They want to see which standard wins before investing effort. Smart, right? Don't waste resources on something that might not matter.
Here's the problem: while you wait, your data stays broken.
The stores waiting for official standards aren't sitting on clean data that's ready to deploy. They're sitting on the same mess they've always had. Inconsistent descriptions. Contradictory specs. Pricing scattered across formats. Category structures that make sense to no one.
When the "official" standard arrives, they won't be ready. They'll be starting from zero. Months of cleanup work ahead of them while early movers are already live.
Waiting for standards doesn't save you work. It delays it. And it guarantees you'll be doing that work under pressure, racing against competitors who started earlier.
The stores building
llms.txttoday aren't betting onllms.txtspecifically. They're betting that AI readability will matter. That bet is safe. The only question is which format. And formats are easy to change when your data is already clean.
Here's why this decision is different from most.
Most business choices involve trade-offs. Spend here, can't spend there. Optimize for this, sacrifice that. Every yes means no to something else.
llms.txt doesn't work that way.
Worst case: AI agents ignore llms.txt forever. Some other standard wins.
Let's call it ai-index.json, because why not. Your competitors panic. Six
months of data cleanup ahead of them before they can even think about the new
format. You open your already-clean data and spend an afternoon reformatting.
Different packaging. Same product. The file becomes irrelevant. The work
doesn't.
Expected case: AI adoption grows gradually. Some agents use your file, some don't. You stay ahead of competitors still debating whether to bother.
Best case: AI agents rely heavily on llms.txt. Your structured, curated
file becomes a competitive moat. While competitors scramble with
auto-generators that produce garbage, you've been refining your data for
months. They start from chaos. You start from clarity.
There is no scenario where you end up worse than where you started. The floor is neutral. The ceiling is significant.
Find another business decision with that risk profile.
That's not a popular thing to say when we offer this as a service ourselves but not every store needs llms.txt. At least not right now.
1. Your products require explanation.
Technical products. Products with specs that matter. Products where the difference between model A and model B isn't obvious from a photo.
Electronics. Outdoor gear. Industrial equipment. Software. Anything where a customer might ask an AI "what's the difference between these two options" and expect a meaningful answer.
If your products have spec sheets, compatibility requirements, or technical documentation, AI needs to understand that information accurately. llms.txt helps ensure it does.
2. Your catalog is complex.
Hundreds of SKUs. Multiple categories. Product relationships that matter. Variants, bundles, accessories, compatibility matrices.
The more complex your catalog, the more chances for AI to get confused. A simple store with twenty products probably doesn't need sophisticated AI optimization. A store with two thousand SKUs across fifty categories is a maze. AI needs a map.
3. You already have content worth indexing.
Buying guides. Sizing charts. Comparison pages. How-to articles. Policy documentation. FAQ sections.
If you've invested in content that helps customers make decisions, that content should be readable to AI. Otherwise you've done the work but AI can't find it. It'll make recommendations based on your product titles while ignoring the detailed guide you spent weeks writing.
4. Your competitors are invisible to AI.
Ask ChatGPT about your product category. See who gets mentioned. If your competitors aren't there either, you have a window. First mover advantage is real when the field is empty.
If AI already recommends your competitors confidently, you're playing catch-up. Still worth doing, but the urgency is different.
1. Your products sell themselves visually.
Fashion. Art. Home decor. Products where the purchase decision is aesthetic, not technical.
AI can describe a dress. It can't convey why this particular shade of blue works. If your customers buy based on how things look rather than what they do, AI optimization is lower priority.
But if you're competing on sustainability claims, material quality, or ethical sourcing, those ARE things AI can get wrong. Know what's driving your customers' decisions.
2. Your business is purely local.
A neighborhood bakery. A local service provider. A restaurant.
AI recommendations matter for products people research. Nobody asks ChatGPT which bakery has the best croissants in their neighborhood. Not yet, anyway. Local businesses have other priorities.
3. Your store has twelve products and a simple structure.
If your entire catalog fits on one page and your policies are straightforward, you don't need sophisticated AI optimization. A basic llms.txt file takes ten minutes to create manually. You don't need our help for that.
4. Your data is a disaster and you know it.
This sounds backwards, but hear it out.
If your product data is fundamentally broken, inconsistent at the source, full of contradictions, llms.txt won't help. Not because the standard doesn't work, but because you need to fix the foundation first.
We can help with that. But it's a different project. Building llms.txt on top of chaos just creates documented chaos. Fix the data first. Then make it AI-readable.
Most e-commerce stores will benefit from llms.txt eventually. The question is timing and approach.
Some stores should implement now and gain early advantage. Some should fix their data first and implement soon after. Some should wait until their business reaches a complexity that justifies the investment.
Three questions cut through the noise:
yeses: move forward now.
yeses: probably move forward, address the weak point along the way.
yes or zero: other priorities first.
We'd rather tell you "not yet" than sell you something you don't need. The stores that come back when they're ready become better clients than the ones we rushed into a project they weren't prepared for.
If you're not sure which category you fall into, that's what the audit is for.
If you've read this far, you're not looking for a plugin. You're looking for clarity.
I don't know your store yet. I don't know if your data is ready or if it needs work first. But I do know this: a 30-minute call will tell us both.
Book a free audit. I'll show you what AI actually says about your store right now. No pitch, no pressure. If it makes sense to work together, we'll talk about that. If not, you'll still walk away knowing something you didn't before.
— Emre