LLMs.txt and the Rise of GEO Astrology: Why You Should Be Wary of AI Optimization Trends

In the rush to optimize for Generative Engine Optimization (GEO), a new trend has emerged: the llms.txt file. Proponents suggest that by creating a machine-readable text file, you can effectively "guide" Large Language Models (LLMs) to understand and cite your content more accurately.

But is this a genuine technical breakthrough or simply GEO Astrology—the act of applying patterns to AI behavior without any scientific evidence?

What is llms.txt and Why Is It Trending?

Similar to robots.txt, the llms.txt proposal suggests providing a simplified, Markdown-based summary of a website specifically for AI crawlers. The goal is to reduce token usage for the LLM and provide a "cheat sheet" for the AI to understand the site's core value proposition.

While it sounds logical on the surface, critical analysis reveals a glaring flaw: the arguments used to justify llms.txt are so vague that they could apply to a file about literally anything—even cats.

The "Cats.txt" Paradox: Debunking the Hype

If the theory is that LLMs prefer structured, simplified text files to understand a site, then a file called cats.txt containing random facts about cats would theoretically "optimize" a site for AI inquiries about feline behavior.

When we realize that the same logic used to sell llms.txt applies to a nonsensical cats.txt file, it becomes clear that we are dealing with GEO Astrology. This refers to the tendency of some marketers to guess how AI models work and sell those guesses as "proven strategies" without access to the actual weights or training protocols of the models.

Why This Matters for Your SEO Strategy

As we transition from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO), the risk of wasting resources on "placebo tactics" increases.

Here is why you should be cautious:

1. Resource Allocation: Spending developer time implementing unproven files like llms.txt takes away from high-impact work like improving E-E-A-T or site speed.
2. False Security: Believing a single text file can "control" an LLM's output ignores the reality of how RAG (Retrieval-Augmented Generation) and training data work.
3. Volatility: AI models evolve rapidly. Tactics based on current "vibes" rather than official documentation from OpenAI, Google, or Anthropic are likely to fail.

Moving Beyond the Hype: Real GEO Tactics

Instead of relying on "astrology," focus on the fundamentals of how LLMs actually retrieve information:

• Structured Data (Schema.org): This is the industry standard for machine readability.
• Clear Information Architecture: High-quality, well-linked content is easier for any crawler—human or AI—to parse.
• Direct Answer Formatting: Using H2s and H3s to ask and answer specific questions directly helps LLMs identify your content as a primary source.