Why is "which LLM do you use?" the wrong question?

Businesses often start evaluating an AI tool by asking which model it uses: GPT or Claude, which version, and whether the latest release will make the product better. Those are reasonable questions, but they reveal little about the tools and information the product can use to complete a task.

Consider the same model, such as Anthropic’s flagship, inside two products. Cursor is a coding agent: given a task through Slack, it can start a virtual machine, clone a repository, write code, run tests and deliver a pull request.

The second is Claude Tag, a "general-purpose teammate" in that same Slack. It reads channel history, remembers project context, connects to your documents and calendars. It can write code too — but it will just as comfortably review a client thread and suggest how to answer a third request for a discount.

The underlying model is the same, but the products give it different roles, tools and context. A coding agent may be able to discuss a customer conversation, yet it will lack the information and workflow of a tool designed for that job.

A harness is the software and configuration around a model: the instructions, tools, context and workflow that turn it into a usable product.

Change the harness, and the same model becomes a different product. Leave the harness weak, and upgrading the model changes almost nothing.

All of this applies not only to the tools you buy, but to how AI sees your business. When a potential customer asks ChatGPT, Claude or Perplexity "which service should I choose for X," the model does not answer from memory. It works inside its own harness: a search tool finds pages, a parser extracts text, and only then does the model compose an answer. At every step, your website either passes the filter or it does not.

A beautiful site built on heavy client-side JavaScript may be invisible to the parser. A page without clear structure — headings, facts, specifics — will be read but never cited: there is nothing for the model to lift. No mentions on external sources means the search step simply will not find you.

This is exactly what GEO (Generative Engine Optimization) is about: optimizing not for a ranking algorithm, but for the harness of generative systems — for how AI agents discover, read and retell your content. Classic SEO asked "how do we get into the top 10 links?" GEO asks "how do we get into the answer itself?"

When choosing an AI tool, check whether its tools, data access and workflow fit the task. These can matter more than whether it uses the most capable model.

When building an internal AI tool, first define the agent’s role, what information it can see, what actions it can take and where it must stop. Choose the model within that design; it can be changed as requirements develop.

For website visibility, consider what an assistant can retrieve and use from your pages. Clear, accessible information gives it useful context when answering a customer’s question.