Stop Fine-Tuning Your LLMs. RAG Exists and It's Not Even Close.

Stop Fine-Tuning Your LLMs. RAG Exists and It's Not Even Close. Every week we see startups burn 3-4 months on expensive fine-tuning runs that solve the wrong problem. We've shipped AI products for fintech, logistics, and SaaS platforms — and the pattern is almost always the same: teams confuse "teaching the model new knowledge" with "changing how the model behaves." These are fundamentally different problems. At Gerus-lab, we've built 14+ AI-powered products and we have a strong opinion: if your first instinct is fine-tuning, you're probably optimizing the wrong thing. Let's tear this apart. The Fundamental Confusion Killing Your AI Project Here's the mental model that saves months of headaches: RAG changes what the model can see right now — at runtime, from external sources Fine-tuning changes how the model tends to behave every time — baked into weights Most teams try to force one tool to do both jobs. That's the mistake. Imagine you're building a customer support bot for a B2B SaaS