Training and Finetuning of LLMs

LLM finetuning is a powerful technique that tailors pre-trained large language models to specific tasks or domains, enhancing their performance and applicability. Unlike prompt engineering, which works within the constraints of a model’s existing knowledge, finetuning involves additional training on a curated dataset to modify the model’s parameters. This process allows the model to learn new patterns, adapt to specific vocabularies, and refine its understanding of particular contexts or tasks.