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Cloud and On-Premises Deployment of Uzbek Legal RAG via Targeted Retriever Fine-Tuning

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-resource languages and under hard operational constraints. We report on building and operating a retrieval-augmented (RAG) legal assistant for Uzbek that must run in two regimes: a managed cloud service that maximizes answer quality within a per-token cost ceiling, and an on-premises deployment for clients whose legal data may not leave their infrastructure, restricting us to open-weight models on limited local hardware under latency constraints.

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