Definition
Data preparation for Large Language Models (LLMs) is the critical process of cleaning, transforming, and organizing raw data to make it suitable for training or fine-tuning AI models. This involves tasks like removing duplicates, handling missing values, standardizing formats, and labeling data accurately to ensure the LLM learns effectively. Proper data preparation is essential for building reliable, accurate, and unbiased LLMs that perform well in real-world applications. For instance, preparing a dataset of customer reviews might involve removing irrelevant text and categorizing sentiment before feeding it to an LLM for analysis. This differs from general data cleaning by focusing specifically on the unique requirements and biases inherent in the vast datasets used for LLM development. Concepts related to data quality and preprocessing for AI appear in domains like AI Fundamentals and Responsible AI.