The emergence of Large Language Models (LLMs) in legal research signifies a transformative shift. This article by Sean Harrington critically evaluates the advent and fine-tuning of Law-Specific LLMs, such as those offered by Casetext, Westlaw, and Lexis. Unlike generalized models, these specialized LLMs draw from databases enriched with authoritative legal resources, ensuring accuracy and relevance. Harrington highlights the importance of advanced prompting techniques and the innovative utilization of embeddings and vector databases, which enable semantic searching, a critical aspect in retrieving nuanced legal information. Furthermore, the article addresses the ‘Black Box Problem’ and explores remedies for transparency. It also discusses the potential of crowdsourcing secondary materials as a means to democratize legal knowledge. In conclusion, this article emphasizes that Law-Specific LLMs, with proper development and ethical considerations, can revolutionize legal research and practice, while calling for active engagement from the legal community in shaping this emerging technology.