[AINews] The End of Finetuning

This article likely discusses how advances in large language models, prompt engineering, and in-context learning are making traditional finetuning less necessary for many applications. It probably explores emerging alternatives to finetuning such as retrieval-augmented generation (RAG), few-shot prompting, and other techniques that allow models to adapt without requiring expensive retraining on custom datasets.

Source: Latent Space | 2026-05-13