Positioned as technique for mastering specific style, tone, or domain logic in AI model adaptation

Clarification article published explaining distinction between fine-tuning and RAG for LLM applications

Release of a newer, more powerful foundation model that outperformed custom fine-tuned solutions

Fine-tuning is argued to be losing its potency as a unique differentiator in favor of data-first approaches