We approached the development of our AI by breaking down the legal research process into steps, mimicking how the best attorneys would tackle a problem.

In legal, there's a significant need for compliance checks, and it's crucial that the in-house team is comfortable with AI handling those tasks.

We adopted a test-driven development approach for our AI prompts, which allowed us to refine the system to achieve high accuracy in responses.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

Achieving 100% accuracy in AI applications is crucial, especially in legal contexts where providing incorrect information can have serious consequences for lawyers handling important cases.

Transforming raw AI models into usable products for lawyers involves significant work, including filtering relevant documents from vast amounts of data.

Real-world testing is essential for ensuring that AI models are reliable and effective in practical applications.

Testing AI agents involves pre-deployment testing with curated conversation sets to ensure expected responses, followed by ongoing evaluation to adapt to changing user interactions and questions.

Adversarial testing involves questioning AI outputs from multiple angles, as the AI will not tire or take offense, allowing for thorough exploration of its responses.