Why Fine-Tune an LLM?

This podcasts explains when it's important to fine-tune large language models (LLMs) to enhance their ability to handle specific tasks or increase their expertise in specific domains. The hosts argue that while LLMs are impressive in their general knowledge, they often lack the depth needed for specialized applications. Fine-tuning, it explains, allows LLMs to be trained on specific datasets, such as legal documents or construction bids, to develop focused expertise in these areas. The hosts then go on to discuss the various types of fine-tuning and even outline the steps involved in this process. Finally, they compare fine-tuning to a complementary technique known as Retrieval-Augmented Generation (RAG), highlighting their different strengths and use cases.