GENERATIVE AI

If you paste your company data into ChatGPT, you did NOT just train it. ❌ I keep getting different versions of this same question: → Can I inject knowledge directly into the model? → Does adding data through RAG actually change how the model thinks? Let's understand the answer with the example of a small company that sells climbing gear. 🧗 They have a return policy, a product catalog, and internal guidelines. They want AI to handle customer questions. If they paste their return policy into ChatGPT – did they train the model? No. They gave it temporary context. The model's brain didn't change at all. If they build a RAG system that retrieves relevant documents when a question comes in – did they train the model? Still no. They built an external bookshelf the model can read from. But the model itself is exactly the same. If they fine-tune the model on their climbing gear data – now they actually changed the brain. But even then, they didn't insert a clean fact into a specific location. The knowledge gets distributed across millions of parameters. There's no single neuron labeled "climbing shoe return policy." 🧠 So what should they actually do? If the goal is for the model to know a specific fact, don't retrain it. Give it through context or external memory. It's cheaper and more controllable. Save fine-tuning for changing behavior like tone, style, reasoning patterns, not for injecting knowledge. I covered all of this and more in a video: → How embeddings work (without the math) → What the latent space actually is → Why reasoning models aren't fundamentally different → When to choose prompting vs RAG vs fine-tuning The mental model I want you to keep: 👉 Parameters = the brain 👉 Training = changes the brain 👉 Embeddings = coordinates for searching meaning 👉 RAG = a bookshelf the brain reads from 👉 Latent space = the internal geometry created by the brain Full video 👇

→ View original post on X — @whats_ai, 2026-03-29 12:00 UTC