19
Nov
In the rapidly evolving world of AI, the ability to customize language models for specific industries has become more important. Although large language models (LLMs) are adept at handling a wide range of tasks with natural language, they excel at general purpose tasks as compared with specialized tasks. This can create challenges when processing text data from highly specialized domains with their own distinct terminology or specialized tasks where intrinsic knowledge of the LLM is not well-suited for solutions such as Retrieval Augmented Generation (RAG). For instance, in the automotive industry, users might not always provide specific diagnostic trouble codes…