Author name: Rachel Ambrose

Our exploration of generative AI in trial preparation has demonstrated how Large Language Models can analyze complex materials to generate sophisticated closing arguments. We began by showing how to overcome traditional LLM length limitations, then demonstrated these techniques by creating closing arguments for both TransOcean and Halliburton in the BP Deepwater Horizon trial. Now we take on perhaps the most challenging perspective: the government’s case against all three defendants.

This article continues our exploration by examining Halliburton’s perspective in the BP Deepwater Horizon Phase One liability trial. As another key defendant in this landmark case, Halliburton’s position offers a distinct viewpoint that demonstrates the versatility of GenAI in crafting nuanced legal arguments from different angles.

As legal organizations race to embrace artificial intelligence, there is an important discussion that needs to take place about the two core deployment options: private Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The issues are focused on implementation and maintenance, security, customization, and costs–vital factors that can either make AI deployment an immediate success or a prolonged challenge. Let’s get to the heart of the matter while outlining what each of these models actually do.