Coheso Team
Coheso Team
Legaltech News covered ILTA Evolve's sessions on AI implementation, featuring insights from Coheso COO Manish Agnihotri on the risks and considerations around fine-tuning large language models for legal applications.
The Fine-Tuning Debate
As legal organizations explore AI adoption, questions arise about customizing models for legal-specific tasks. Fine-tuning—training models on proprietary data—offers potential benefits but introduces significant risks that Manish addressed in the coverage.
Key Considerations for Legal AI
The Legaltech News piece highlighted several important points:
- Data quality matters — Fine-tuning on poor-quality data can degrade model performance
- Security implications — Training data may inadvertently leak through model outputs
- Maintenance burden — Fine-tuned models require ongoing updates and monitoring
- Alternative approaches — Retrieval-augmented generation (RAG) can deliver customization without fine-tuning risks
Coheso's Approach to AI Customization
Rather than relying heavily on fine-tuning, Coheso employs retrieval-augmented generation to incorporate organization-specific knowledge. This approach:
- Keeps sensitive data separate from the base model
- Allows rapid updates without retraining
- Provides transparency into information sources
- Maintains security with enterprise-grade data handling
Why This Matters for Legal Teams
Legal departments handle sensitive information daily. The architecture decisions underlying AI tools have real implications for security, accuracy, and compliance. Understanding these trade-offs helps legal operations leaders make informed technology choices.
ILTA Evolve Context
ILTA Evolve brings together legal technology professionals to discuss emerging trends and best practices. The 2024 conference reflected intense interest in AI implementation, with sessions covering everything from practical applications to governance frameworks.
This story was originally covered by Legaltech News. Read the full article →