How do I become visible in ChatGPT answers?
This is the new puzzle content marketers are trying to solve. Not just for ChatGPT, but for every major LLM out there: Perplexity, Gemini, Claude, Deepseek, and more.
There’s no guaranteed way to make it happen yet, but there are clues; patterns that might just lead us in the right direction.
Of course, the usual SEO playbook still applies: structured data, in-depth content, backlinks. But there’s something new that’s gaining attention, a potential cheat code for AI visibility: llms.txt.
Marketers are already noticing traffic coming from LLMs, but how do you consistently get cited, referenced, or used in AI-generated answers? Instead of relying on guesswork, this article looks into a technical strategy that might benefit you.
llms.txt is still in its early days, not yet a standard, but definitely a concept being explored by higher-ups in AI research. Could this be the future of AI-SEO? Let’s find out.
Understanding llms.txt in an AI-SEO Context
What is llms.txt?
Imagine you’re at a restaurant, and you tell the chef you want “spicy pasta.” If the chef only relies on their past training, they might make it the way they were taught years ago—maybe Italian-style spicy, maybe Indian-style, or something else entirely.
Now, what if you hand them a small note that says: "Use extra chili flakes, no cheese, and serve with garlic bread."
With this real-time instruction, the chef now knows exactly how to customize your dish based on your preferences without having to retrain on an entirely new recipe.
That’s exactly what llms.txt does for LLMs (Large Language Models).
If we go by the technical definition:
llms.txt is a framework that acts like a set of detailed instructions for LLMs during inference. It tells the model what information to pay attention to and which paths to follow, without changing the model’s core training. This extra context can help the model deliver answers that are more accurate and complete.
Uttam Tripathi
Does it change what AI knows about you?
Well, no! And that’s the catch.
It isn’t designed to change what AI ‘knows’.
It just influences how AI responds to the user's question.
It doesn’t rewrite their past knowledge, but it shapes how they present information at that moment.
So, does llms.txt make an AI remember you, your brand, or your content permanently?
No, it only works at inference time, meaning the AI follows the instructions while generating a response, but doesn’t store or retain that information for future interactions.
For marketers and content creators, this means you can guide AI-generated content without changing the AI itself, a powerful way to improve how your brand is represented in AI-driven search and responses.
What could be some potential benefits of llms.txt in AI-SEO?
To begin with, here are some immediate benefits:
- Improved factual accuracy: The extra instructions help the model stick to verified details.
- Enhanced completeness: More context means the answer covers all the important points.
- Better relevance: The output is more on target with what was asked. All these factors contribute to better content, which can lead to higher rankings and more engagement.
But by doing all these, you are actually providing clear instructions to AI model, and as such the AI model might prioritize your data over other sources. We can eventually use llms.txt to nudge AI models toward using their sources without requiring retraining. It’s an emerging strategy, but one that could shape the future of AI-SEO.
Experimentation overview
To understand whether llms.txt truly makes a difference in AI-generated responses, I set up a straightforward test:
- I picked 21 different prompts, a mix of technical and general questions related to product-specific queries.
- For each prompt, I generated two responses:
- One with llms.txt → This version used extra instructions to guide the AI.
- One without llms.txt → A standard response without any extra context.
- I then compared both versions side by side to see if llms.txt actually improved the quality of answers.
- Using gemini-1.5-pro-002 model.
It was like testing whether giving a chef detailed recipe notes (llms.txt) makes a dish more accurate compared to them cooking from memory alone (no llms.txt).
I actually did a short test for 10 different questions too, whose finds are in this post:
Evaluation metrics: How I measured the difference
I didn’t just rely on intuition to judge whether one answer was better than the other. Instead, I evaluated them based on four key metrics that matter for AI-generated content:
1. Factual Accuracy – How correct is the answer?
- Does the response contain verifiable, correct information?
- Does it avoid hallucinations (AI making up facts)?
2. Clarity – How easy is it to read and understand?
- Is the response concise yet informative?
- Does it avoid overcomplication or unnecessary jargon?
3. Completeness – Does the response cover all necessary points?
- Does it fully answer the question, or does it leave gaps?
- Is the response more detailed and useful compared to the other version?
4. Relevance – Does the answer stay on topic?
- Does the response directly address the question without going off-track?
- Is the extra context actually useful, or is it fluff?
Each response was assessed against these criteria, and the scores were recorded into a CSV file for further analysis.
Insights: What the results tell us
Our experiment used 21 different questions to compare two sets of AI-generated responses—one using llms.txt and one without it. We evaluated each answer on four key points: factual accuracy, clarity, completeness, and relevance.
What did we find?
Here is a radar chart covering all four parameters for both types.
1. Factual accuracy, completeness, and relevance improved
- Factual Accuracy: The answers with llms.txt generally had more correct details. Think of it as having a cheat sheet that reminds the AI of the right facts, so it doesn’t make things up.
- Completeness: With llms.txt, the responses were more complete, they covered more of the important points. For example, when asked about how LTM-2 boosts workflow, the extra guidance helped the AI include several key details that were missing otherwise.
- Relevance: The responses stayed more on topic when llms.txt was used. This means the extra instructions helped the AI focus on what was really important in the question.
2. Clarity was a mixed bag
Clarity: Here, the results were less consistent. In some cases, the extra context made the answers longer and a bit harder to read. Imagine getting a very detailed explanation that, while full of good information, feels a bit too wordy or cluttered.
Average score values
Note: You’ll find the completeness score on average is lower in llms.txt
You might notice that the average completeness score for responses using llms.txt is lower. This is because our llms.txt file only included about one-quarter of the total Pieces information we intended, due to a 250KB size limit (original ~ 1mb).
In other words, the llms.txt we used didn’t have all the details about Pieces, whereas the non-llms.txt responses draw from the model’s full training data, which likely contains a more complete set of Pieces information. This difference in available context naturally affects how complete the answers are.
Score difference by question & criterion
Why does this happen?
Prompt dependency:
Not every question is the same. For simple, straightforward questions, adding extra instructions might not help much, in fact, it could even make the answer more complicated than needed. But for tougher or more detailed questions, llms.txt really shines by guiding the AI to include all the right details.
Trade-off in output style:
Using llms.txt is like giving someone detailed directions before they speak. It makes sure they cover all the necessary points (improving accuracy and completeness) but can also lead to longer, sometimes less clear explanations. In other words, the AI’s “voice” becomes richer but occasionally less crisp.
Conclusion
At a general level, this experiment shows that llms.txt can improve the quality of AI responses, boosting factual accuracy, completeness, and relevance. The added instructions act as a guide for the model, ensuring it picks up the right details and stays focused on the prompt.
The real question now is whether LLMs will start recognizing and using llms.txt as a standard practice.
It certainly looks like things are moving in that direction. As more models emerge, having a centralized reference point like llms.txt could become essential, leveling the playing field for everyone.
In short, while llms.txt isn’t a magic bullet yet, it’s a promising step toward more precise and effective AI-SEO. The next phase will be to refine its content further and see if it can eventually become the standard for guiding LLM outputs.
What do you think, will llms.txt become a mainstream tool in AI content creation?
Disclaimer:
The experiments and analyses presented in this article were conducted solely by the author and represent preliminary findings. These results are provided for informational purposes only. Any use or dissemination of these findings in further research, publications, or derivative works must include proper attribution to the original work and the author.
