Large language models (LLMs) such as GPT-4, Llama, and Gemini are some of the most significant advancements in the field of artificial intelligence (AI), and their ability to understand and generate human language is transforming the way that humans communicate with machines. More than 70% of companies use AI in their business functions, according to McKinsey. LLMs are pretrained on vast amounts of text data, enabling them to recognize language structure and semantics, as well as build a broad knowledge base that covers a wide range of topics. This generalized information can be used to drive a range of applications, including virtual assistants, text or code autocompletion, and text summarization; however, many fields require more specialized knowledge and expertise.

A domain-specific language model can be implemented in two ways: building the model from scratch, or fine-tuning a pretrained LLM. Building a model from scratch is a computationally and financially expensive process that requires huge amounts of data, but fine-tuning can be done with smaller datasets. In the fine-tuning process, an LLM undergoes additional training using domain-specific datasets that are curated and labeled by subject matter experts with a deep understanding of the field. While pretraining gives the LLM general knowledge and linguistic capabilities, fine-tuning imparts more specialized skills and expertise.

LLMs can be fine-tuned for most industries or domains; the key requirement is high-quality training data with accurate labeling. Through my experience developing LLMs and machine learning (ML) tools for universities and clients across industries like finance and insurance, I’ve gathered several proven best practices and identified common pitfalls to avoid when labeling data for fine-tuning ML models. Data labeling plays a major role in computer vision (CV) and audio processing, but for this guide, I focus on LLMs and natural language processing (NLP) data labeling, including a walkthrough of how to label data for the fine-tuning of OpenAI’s GPT-4o.