“Should we train our own AI?”
(The answer is usually no.) To make the right decision, you need to understand how AI learns your business data. Here is the breakdown. 👇 Foundation Model 🏗️ A general-purpose model (like GPT-4) pre-trained on the public internet. It knows “everything about everything,” but nothing about your company. Fine-Tuning 🎓 Taking a Foundation Model and training it further on your specific data. Analogy: Sending a smart student to medical school to learn a specialty. It changes how the model talks and behaves. RAG (Retrieval-Augmented Generation) 📚 Connecting a standard model to your live company documents. Analogy: Letting the student take the test with an open textbook. They don’t memorize the data; they look it up when you ask. Most businesses need this, not fine-tuning. Hallucination 👻 When an AI confidently states a fact that is completely false. (This happens often when you force it to memorize data rather than look it up). Grounding ⚓ The process of anchoring AI responses in verifiable facts or real-world data to prevent hallucinations. That said recently AI companies are now suggesting that the next step might actually be training the models on your company data.