Publication Date

Spring 2024

Document Type

Article

Abstract

The article discusses the transformative impact of generative AI large language models (LLMs) on legal research. Callister explores how these AI models, despite their current imperfections, are poised to shift the cognitive (or trusted) authority within the legal profession. He attributes this shift to the ease with which AI processes vast amounts of legal information and the anthropomorphic design of LLMs, which fosters trust among users. Callister introduces the concept of Retrieval-Augmented Generation (RAG), explaining how AI platforms integrate legal texts into their responses, enhancing their reliability over purely generative models. Through various examples, he demonstrates the strengths and limitations of using AI in legal research, highlighting issues such as AI "hallucinations," misinterpretations, and the necessity of human oversight. The article underscores the need for cautious adoption, emphasizing that while AI can serve as a valuable starting point, traditional legal research methods remain essential. Callister concludes that the evolving landscape of AI in legal research will likely see significant advancements, making it an intriguing field to watch in the coming years.

Publication Title

Generative AI Large Language Models and Researching the Law

Comments

The "related resource" is a much longer article targeted to an academic audience.

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