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25 I L T A N E T . O R G based on features from the text, helping identify outliers or unusual terms that might need closer examination. Performance metrics for these models are necessary to assess how well they work. Transformer-based models, which can understand the context within the text, usually perform better in legal text classification processes. Model evaluation methods include accuracy, precision, recall, and F1-score metrics. Typically, transformer-based models achieve higher scores due to their ability to grasp the complex legal language and its nuances. Legal text analysis widely uses ML models like Support Vector Machines (SVMs), Random Forests, and transformer-based models like BERT. Supervised learning methods, which need labeled data, are used for tasks like extracting clauses from contracts. For example, models are trained on annotated contracts to identify and categorize specific clauses, speeding up the review processes. Unsupervised learning methods, which don't require labeled data, are used for clustering and detecting unusual contract patterns. Algorithms like K-Means clustering group similar contracts T he legal industry has recently been experiencing a technological transformation driven mainly by advances in artificial intelligence (AI). These innovations and breakthroughs are not just automating routine tasks or making the execution of complicated legal tasks easier but fundamentally changing how legal professionals do their work. From document review and analysis to automating and enhancing the search process and even predicting litigation outcomes, AI is helping lawyers and legal professionals work more efficiently and make better decisions. This article explores some of these developments, current technology usage, advanced applications of large language models, new use cases, technical challenges, ethical considerations, future developments, and some practical guidelines. THE CURRENT TECHNOLOGY STACK IN LEGAL PRACTICE Machine learning (ML) models in legal workflows have significantly changed document review processes. Algorithms that can process large amounts of data quickly and accurately are rapidly replacing traditional methods that rely on manual reading. FEATURES

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