The Fundamental Misunderstanding of Context in an AI World

Content does not equal Context.

Starting with the assumption that adding content to a site is enough to be accurately represented in LLM responses. The reality is math does not care about your content. AI leverages context to provide content to a user, not the other way around.

Gone are the days of targeting keywords. The latest hype is targeting prompts. This is a fundamental misunderstanding that will continue to be overlooked. Context lets a model infer what the user actually means, not just what they’re typing.

Let’s look at a trivial example:

You and I both type, “best banana bread recipe.”

We see different recipes.

The model already knows your gluten-free from past conversations.

Your “best” isn’t the same as my best.

You’ll see, “Do you want a gluten-free recipe?”.

I’ll just see a recipe.

That small difference is context in action.

Now apply that framework to your business.

See the difference?

False Assumptions

Most companies assume that placing their FAQs, help docs, or product descriptions on their site is enough to satisfy LLMs, but that is content, not context.

Context is the connection between matrices (data points). If your data is nothing more than a flat list of answers, it can’t distinguish you from your competitors. But when your data expresses relationships (think SQL tables), how your products connect to specific customer needs, or how your expertise applies in different scenarios, the model learns to understand when you’re the right answer.

That’s how you get recommended.

Not by keywords, not by prompts, but by clarity of context.

Ask yourselves, in what context does my business provide a solution?