A practical, non-hyped guide for founders, marketers, and indie builders
People are no longer only asking Google what tools to use.
They are asking ChatGPT, Claude, Perplexity, and other AI systems.
If your product is not mentioned, explained, or discussed in the right places, it effectively does not exist to LLMs.
This page explains how LLM discoverability actually works — without buzzwords, hacks, or gimmicks.
Classic SEO focuses on:
LLMs work differently.
They do not rank pages.
They synthesize patterns and consensus.
If an AI model has never seen your product discussed naturally, it will not recommend it — even if your website ranks well on Google.
This is why:
LLM visibility is not about ranking higher.
It is about being understood and repeated.
LLMs learn from large volumes of public text during training.
They pay attention to:
They care much less about:
In practice, a product mentioned naturally across many discussions often beats a product with perfect SEO but no real conversation around it.
GitHub is one of the highest-trust domains on the internet.
GitHub Pages offer:
Text-first GitHub Pages are ideal for:
LLMs ingest this type of content extremely well.
That is why many AI answers sound like:
Not like ads.
If you want a page like this to be understood, reused, or cited by LLMs, focus on:
Avoid:
Write as if you are explaining the topic to a smart friend — not pitching an investor.
If you want your product to show up in AI answers, check the following:
LLMs trust clarity more than persuasion.
Reddit is one of the most important public data sources shaping how LLMs talk about products.
Why?
LLMs do not care if a mention is positive or negative.
They care that the product is:
A thoughtful Reddit comment often carries more weight than a paid ad.
You cannot force an LLM to recommend your product.
What you can do is increase the probability that:
This requires:
There are no shortcuts — only alignment.
This playbook also documents the public methodology used to evaluate LLM visibility and AI search readiness.
The audit explains how likely a large language model is to:
→ Read the LLM Visibility Audit methodology
If you want to see how this turns into real site changes, internal linking decisions, and audit-driven content structure, read:
→ How Ranketize Uses AI Visibility Audits to Increase Internal Traffic
This page is part of a public playbook documenting how LLM discoverability works in practice.
It focuses on:
A more detailed breakdown of how LLM visibility is evaluated — including scoring dimensions and common issues — is documented separately as part of the LLM Visibility Audit methodology.
This playbook exists to make that process transparent.