General-purpose large language models excel at breadth. Domain-specific applications demand depth — and achieving that depth requires deliberate fine-tuning strategies aligned with the unique constraints of legal and medical data.
The Domain Adaptation Challenge
Legal and medical language is dense with jargon, carries high stakes for precision, and is governed by strict regulatory requirements around data use. Standard fine-tuning approaches that work well for general text often underperform when applied to these domains without modification.
Key Techniques
- Retrieval-Augmented Generation (RAG): Grounding model responses in verified source documents reduces hallucination in high-stakes contexts.
- LoRA and Parameter-Efficient Fine-Tuning: Adapting models on limited domain data without catastrophic forgetting of general capabilities.
- Constitutional Constraints: Encoding domain-specific rules (e.g., legal citation format, clinical terminology standards) as hard constraints during decoding.
Case Studies
Our analysis covers two deployments: a contract review system for a mid-market legal firm that reduced review time by 60%, and a clinical documentation assistant that improved coding accuracy from 74% to 91% on ICD-10 assignments.