Large Language Models (LLMs) are advanced neural networks with billions of parameters, trained on extensive text data using semi-supervised learning. They excel at tasks such as sentiment analysis and mathematical reasoning, allowing them to understand and generate human language effectively. LLMs are particularly useful for analyzing unstructured data like customer reviews and social media interactions, providing businesses with valuable insights.

LLMs are designed to handle unstructured data, such as text files, while traditional analytics methods like decision trees are more suited for structured data in tabular form. LLMs require less data preprocessing and feature engineering, as they automatically learn patterns from raw text. However, they face challenges in interpretability, making it difficult to understand their decision-making processes.

Businesses leverage LLMs for various applications, including sentiment analysis to gauge customer feedback, Named Entity Recognition (NER) for identifying key entities in text, and text generation for creating chatbots. These applications help optimize workflows, enhance customer engagement, and improve fraud detection, ultimately leading to more informed business decisions.