Enhancing Legal Document Analysis with Large Language Models: A Structured Approach to Accuracy, Context Preservation, and Risk Mitigation

Abstract

The growing complexity and volume of legal documents, particularly contract agreements, pose significant challenges for effective analysis. This study explores the application of OpenAI’s large language model API to processing lengthy legal contracts, using a case study of an agreement between the Palm Springs Unified School District (PSUSD) and the City of Palm Springs. I identify key challenges in legal document processing—including context window limitations, optimal segmentation of text, maintaining contextual coherence across sections, and accurate summarization—and examine how modern AI and NLP techniques address these issues. The methodology combines hierarchical segmentation of the contract with chain-of-thought prompting and multi-stage summarization techniques to overcome token limits and preserve context. Results indicate that OpenAI’s API (exemplified by GPT models) can effectively summarize and analyze long contracts, capturing critical obligations and clauses with high accuracy and efficiency. The case study demonstrates improved processing speed and comparable accuracy to human legal analysts for summarization tasks, aligning with recent benchmarks in legal AI performance discussed in this paper is how these AI-driven methods, grounded in advanced linguistic capabilities, are transforming legal language analysis by making legal content more accessible and highlighting ambiguities and obligations automatically. Ethical considerations—such as confidentiality, bias, and the risk of AI hallucinations—are also addressed, alongside practical applications of this approach in legal practice. I conclude with reflections on the implications for modern linguistics and legal professionals, acknowledging current limitations and proposing directions for future research in AI-assisted legal document analysis.