Image: John Tredennick, Merlin Search Technologies with AI.
In our recent articles exploring how generative AI can transform trial preparation, we demonstrated how Large Language Models (LLMs) could analyze complex trial materials to generate sophisticated closing arguments. Our first piece, “Understanding GenAI Response Limits: What Every Legal Professional Should Know,” introduced techniques for overcoming traditional LLM length limitations. We followed this with an analysis of TransOcean’s closing argument, showing how AI could process vast amounts of testimony and trial exhibits to construct compelling arguments. Creating a Compelling Closing Argument in the BP Trial: Part One–TransOcean’s Closing.
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.
The Context
For those unfamiliar with Halliburton’s role in the Deepwater Horizon disaster, some context is helpful. Halliburton served as the cementing contractor for the Macondo well, providing specific technical services under BP’s direction. When the well suffered a catastrophic blowout on April 20, 2010, resulting in eleven deaths and unprecedented environmental damage, Halliburton faced significant liability alongside BP and TransOcean.
Leveraging GenAI for Multiple Perspectives
Using our DiscoveryPartner platform, we processed the same trial materials used for TransOcean’s argument but focused the analysis on Halliburton’s position. This demonstrates a key advantage of GenAI in trial preparation—the ability to quickly analyze vast amounts of evidence from different strategic angles.
This demonstrates a key advantage of GenAI in trial preparation—the ability to quickly analyze vast amounts of evidence from different strategic angles.
John Tredennick and Dr. William Webber, Merlin Search Technologies.
1. Building the Outline for Halliburton’s Closing Argument
Our first step was to create an outline for Halliburton’s closing argument. Here was the outline Sherlock AI created as a starting point (with links to relevant transcript sections and exhibits):
2. Building the Argument Section by Section
After establishing our outline, we systematically built each section of the closing argument using DiscoveryPartner’s AI capabilities. For each, we initiated three kinds of AI-based searches (semantic, AI keyword and ML classifier) across our trial record, allowing the system to identify and analyze the most relevant testimony and exhibits. The platform’s sophisticated AI engines identified approximately 700 highly relevant document sections for each argument component, evaluating these materials for both factual support and strategic value to Halliburton’s position.
The platform’s sophisticated AI engines identified approximately 700 highly relevant document sections for each argument component, evaluating these materials for both factual support and strategic value to Halliburton’s position.
John Tredennick and Dr. William Webber, Merlin Search Technologies.
What makes this approach particularly powerful is how it synthesizes information across multiple sources, identifying patterns and connections that support key legal arguments. The AI then weaves these elements together into coherent, well-supported narrative sections that maintain consistent themes while integrating specific evidence from the trial record.
To keep this article at a decent length and to show the results of our work, here are the first two sections of Halliburton’s proposed closing argument. While we only offer this as a good first draft, you can quickly see how Sherlock AI has synthesized evidence from a broad swath of testimony and exhibits and constructed a compelling narrative about Halliburton’s limited role and BP’s ultimate responsibility. You can access the complete 15-page GenAI generated closing argument here.
The opening sections effectively established several key themes that run throughout Halliburton’s defense:
- Clear delineation of Halliburton’s limited contractual role
- Documentation of BP’s comprehensive control over operations
- Evidence of BP’s systematic prioritization of cost over safety
- Specific examples of BP overriding contractor safety recommendations
3. Stitching the Sections Together
The final step was to combine the sections into a final closing argument. This is a simple step that is accomplished by pasting the output from the LLM and pasting it into a Google document. Most LLMs output their answers in a Markdown format which you can read about here. One handy feature in Google docs is the paste from Markdown option.
Thus, the creation of Halliburton’s closing argument followed the same systematic approach we outlined in our previous articles:
- Document Integration
a. Compiled complete trial repository including transcripts and exhibits
b. Organized materials for AI analysis using DiscoveryPartner
c. Promoted document sections relevant to Halliburton’s defense strategy
- AI Analysis
a. Deployed multiple LLMs to analyze trial materials
b. Used sophisticated prompt engineering to focus on Halliburton’s perspective
c. Generated initial argument structure based on key defense themes
- Evidence Integration
a. Automatically linked citations to source documents
b. Verified factual assertions against trial record
c. Maintained coherent narrative flow while integrating technical details
We hope these examples will help demonstrate several key benefits of using GenAI for trial preparation:
- Rapid Analysis: The system processed thousands of pages of trial materials in minutes.
- Multiple Perspectives: Generated distinct arguments for different parties using the same evidence.
- Comprehensive Coverage: Identified and integrated relevant evidence across the entire trial record.
- Cost Efficiency: Produced sophisticated first drafts at a fraction of traditional costs.
The Path Forward
Our work with the Halliburton closing argument further validates the transformative potential of generative AI in trial preparation. By successfully analyzing the same trial materials from a different strategic perspective, we’ve demonstrated that this technology can adapt to the nuanced requirements of complex multi-party litigation while maintaining rigorous analytical standards.
By successfully analyzing the same trial materials from a different strategic perspective, we’ve demonstrated that this technology can adapt to the nuanced requirements of complex multi-party litigation while maintaining rigorous analytical standards.
John Tredennick and Dr. William Webber, Merlin Search Technologies.
The ability to rapidly generate sophisticated legal arguments from different viewpoints represents a significant advancement in trial preparation. Using platforms like DiscoveryPartner, legal teams can now explore multiple strategic approaches in the time it traditionally took to develop a single argument. This efficiency doesn’t come at the cost of quality – rather, it allows lawyers to focus their expertise on refining and perfecting arguments built on comprehensive analysis of the complete trial record.
This technology is ready for deployment in real-world litigation. Our success in generating compelling arguments for both TransOcean and Halliburton demonstrates that GenAI can effectively handle the complexity of major litigation while adapting to the specific needs of different parties. The key lies in combining AI’s analytical power with human legal expertise, creating a synergy that enhances both efficiency and effectiveness.
In our next article, we’ll examine how GenAI can assist in constructing arguments for plaintiffs in the BP trial, showing how this technology can serve both sides of the courtroom. The future of legal practice lies in this thoughtful integration of artificial and human intelligence, where technology amplifies rather than replaces professional judgment.