Srijith Rajamohan
Dr. Srijith Rajamohan currently leads AI Research at Redis for building efficient and scalable retrieval systems with GenAI. Prior to this role, he has led the data science effort for Sage Copilot and also led the team that created and deployed domain-specific LLMs to address the deficiencies of off-the-shelf models for accounting. He also had stints at Databricks where he led the data science developer advocacy efforts and at Nerdwallet as a data scientist. Before making the switch to the tech sector, he spent about six years in academia as a computational scientist at Virginia Tech.
Sessions
Large Language Models (LLMs) have opened new frontiers in natural language processing but often come with high inference costs and slow response times in production. In this talk, we’ll show how semantic caching using vector embeddings—particularly for frequently asked questions—can mitigate these issues in a RAG architecture. We’ll also discuss how we used contrastive fine-tuning methods to boost embedding model performance to accurately identify duplicate questions. Attendees will leave with strategies for reducing infrastructure costs, improving RAG latency, and strengthening the reliability of their LLM-based applications. Basic familiarity with NLP or foundation models is helpful but not required.