Performing Automated Employment Law Case Analysis Using Large Language Models

In pursuing agentic AI for complex legal workflows, systems must deliver accurate, cited, and nuanced outputs—boiling down to accurate legal question answering (QA). This project focuses on performing QA in Canadian employment law, focusing on worker classification (e.g., employee vs. contractor tests from Sagaz Industries precedents), using the Sagaz dataset: over 300 case analyses from David Liang and Samuel Dahan's Conflict Analytics Lab. One of the limitations of Large Language Models (LLMs) is that in analyzing large 30-page or longer documents to answer questions, even though all the text is in the context window, key pieces of information may still be missed in answering questions. To address this limitation, we explore various Retrieval-Augmented Generation (RAG) techniques to provide Large Language Models (LLMs) with the relevant context for 20+ case-specific questions that make up the case analysis. RAG techniques include no-processing (full case as context), chunking with vector embeddings for efficient retrieval, knowledge graph construction for Graph RAG to capture relational elements, and combinations of different methods. Additionally, methods for performing the analysis of multiple questions in a single prompt are also being explored to balance token usage efficiency and accuracy. Preliminary evaluations have with the no-processing technique has shown some signs of limited success of 50% accuracy on a 20-case subset of cases in the dataset. Work is currently still ongoing: to improve the prompts used to specify the case analysis questions as mis-interpretation and overly strict evaluations were a leading cause of incorrect answers in these initial tests; to scale up the processing pipeline (as some of the processing techniques take up many millions of LLM tokens); and to analyze the resulting outputs across 20+ questions asked in each case analysis for the hundreds of cases.