Why You Should Care About How Base Models Reflect Your Brand

In today’s AI-driven world, large language models (LLMs) like GPT-4o, LLaMA, and Gemini 2.5 are becoming core to how information is discovered, summarized, and delivered to audiences. While it matters on what happens when you type a prompt into ChatGPT or similar tools, there’s an earlier and more influential stage that often gets overlooked: how your brand is represented in the *base models* that power these systems. In his talk "Software is changing (again)", Andrej Karparti described LLMs as the operation system that drives the new generation of software. You should understand what that base LLM thinks about you! 

Before any prompt is answered, LLMs have already been “pre-trained” on huge volumes of data. This training determines how they understand and describe your brand. If your brand is missing, outdated, or misrepresented here, that baseline perception will influence every AI-generated mention going forward. This affects chatbots, a search result, or an AI-powered tools. 

When a base model understands your brand well, AI tools are more likely to produce consistent, on-message results. If there are gaps or inaccuracies in the training data, the model may generate answers that feel off-brand or even misleading. By identifying these weaknesses early, you can address them before they scale into public-facing content.

This isn’t about chasing one prompt at a time. It’s about shaping the *foundation* of how AI understands your brand so that it can accurately and positively represent you in every future interaction. As AI embeds itself into more channels and touch points, the base model becomes the silent but powerful force behind your brand’s visibility and perception.