Enhancing Legal Precision and Compliance with RLHF

Challenge

A legal services team adopted generative AI to accelerate contract drafting and document review. While the system produced fluent outputs, the responses were often too generic and missed the firm’s policy and jurisdiction-specific nuances. This resulted in heavy downstream review, increased billable hours for low-value tasks, and heightened risk of regulatory non-compliance.

DDD’s Solution

DDD applied Reinforcement Learning from Human Feedback (RLHF) to align the AI system with precision. SMEs and compliance specialists reward modeling on firm-specific documents, capturing factual precision, policy adherence, and professional tone. These labels trained reward models to guide AI decision-making. Our multilingual evaluators with legal expertise conducted pairwise scoring and rubric-based reviews, ensuring responses met regulatory standards and respected client confidentiality. The RLHF pipeline was stress-tested against edge-case scenarios, including adversarial legal prompts, to reduce hallucinations while maintaining fluency and accuracy.

“Transforming a generic AI drafting tool into a policy-aware legal assistant with RLHF”

Impact

After RLHF optimization, the client saw:

  • 35% reduction in review time, enabling attorneys to focus on strategic advisory and litigation preparation rather than repetitive corrections.

  • Significant gains in compliance alignment, with outputs consistently adhering to firm policy and regional legal frameworks.

  • Attorneys reported fewer escalations to senior partners and greater confidence in draft documents.