RAG or Fine-Tuning? What Real Client Projects Taught Us About Choosing

Almost every enterprise AI conversation I have reaches the same fork within the first hour. The client has a large language model that is impressive in a demo but does not know their products, their policies or their way of writing. Someone in the room asks: should we fine-tune the model on our data, or should we build retrieval-augmented generation (RAG) around it? The honest answer is that the question is usually framed wrong. RAG and fine-tuning solve different problems. Teams that treat…

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RAG or Fine-Tuning? What Real Client Projects Taught Us About Choosing