Abstract: This paper presents the design and implementation of LEGAL AI, an artificial intelligence-powered legal research and case prediction system customized for the Indian judicial context. It leverages a fine-tuned LLaMA-2 model and InLegalBERT using transfer learning and domain adaptation to provide functionalities such as case outcome prediction, legal explanation generation, and legal question answering (Legal QA). The system employs a Streamlit interface and FAISS-based vector search to retrieve relevant legal documents and provide contextual legal insights. With domain-specific fine-tuning and quantized models for CPU inference, LEGAL AI enhances accessibility, interpretability, and efficiency in legal research and decision-making.
Keywords: Legal AI, LLaMA-2, InLegalBERT, Legal Question Answering, Indian Judiciary, FAISS, Domain Adaptation , Retrieval-Augmented Generation.
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DOI:
10.17148/IJARCCE.2025.14573
