Case Study
Multi-LLMRAGSystem
Democratizing access to complex legal and administrative data. Architected a scalable RAG pipeline supporting dynamic model selection (GPT-4, Claude, Llama) with parallel embeddings.
The Challenge
Legal queries vary wildly in complexity. Using a single large LLM for all queries was cost-prohibitive and slow, while smaller models hallucinated on nuanced constitutional questions.
The Solution
Engineered a dynamic routing RAG architecture. Simple queries are handled by local Llama 3 models, while complex comparative analysis is routed to GPT-4 or Claude. Embedded documents using parallel processing.
The Results
- Decreased average query latency by 60%.
- Cut API inference costs by 75% compared to a pure GPT-4 setup.
- Maintained 99% hallucination-free responses on legal benchmarks.