Do we need fine-tuning for enterprise AI?

Question

Accepted Answer

Often no. Many use cases are solved with retrieval, prompt design, tool usage and evaluation before fine-tuning becomes necessary. Fine-tuning is valuable in some situations, but it is often overused too early. In many business cases, better retrieval and output constraints create more immediate gains. A pragmatic approach is to start with the simplest architecture that can be measured, then decide later whether fine-tuning is justified by the use case.