Author: Julian Bryant

This custom system uses A.I. language models to generate client-specific hypothetical scenarios, then evaluates document performance against stated client priorities.

My Work Evaluating A.I. Language Model Performance for Legal Reasoning TLDR: Quick Summary Language models power A.I. tools that can substantially boost attorney productivity such as ChatGPT, Anthropic’s Claude, and Google’s NotebookLM.[1]The MMLU benchmark is a standard test for evaluating...

From document analysis to drafting assistance, AI tools offer compelling benefits. However, before we rush to adopt them, we need to ensure they meet the high standards our clients expect and deserve.

Using Graph Databases to Enhancing Legal Document Analysis: From Vector-Similarity Limitations to Graph-Based Innovation In the realm of legal technology, the integration of Large Language Models (LLMs) has been a game-changer for analyzing and understanding complex legal documents. These innovative...

Creating a Python-Based Document Q&A App Using OpenAI Language Models Part 1 With the previous article about natural language processing (NLP) and large language models (LLMs) as background, I’d  like to walk you through an example of using an LLM to...

Using Language Models to Understand Complex Documents (Part 2: Results) In the previous article I described the architecture of a system that enables a user to ask questions of a long, complex document. As I generate complex legal documents for...

Development of Language Models to Process Legal Language One possibility that has intrigued me for years at the intersection of law and technology has been getting computer systems to “understand” the substance of all kinds of contracts. This could open...