RAG vs fine-tuning, decoded
For almost all business needs, use RAG. It grounds the model in your documents at question time, cuts hallucinations 70–90%, and updates instantly. Fine-tuning is for changing a model’s style or behavior, not for feeding it facts.
Which technique fits which goal
Use RAG when the goal is accurate answers from your knowledge; use fine-tuning when the goal is changing how the model writes or behaves. Most business goals are the former, which is why RAG is the default.
The decision is about the goal. If you want the AI to answer correctly from your policies, contracts, or knowledge base, that is a retrieval problem, RAG, and it cuts hallucinations 70–90% while staying current as your documents change. If you want the model to adopt a specific tone or specialized format consistently, that is a behavior problem where fine-tuning can help. Vendors often blur this; “trained on your data” usually should mean RAG, which is cheaper and safer than retraining.
Frequently asked questions.
Can I use both RAG and fine-tuning together?
Yes, and sophisticated systems do, fine-tuning the model’s behavior or format while using RAG to supply current facts. But start with RAG alone; it solves the most common need (accurate, grounded answers) at a fraction of the cost and complexity of fine-tuning.