Dive into what is LLMOps

In a podcast episode featuring Rohit Agarwal from Portkey and Connor from Weaviate, the discussion delves into the distinctions between MLOps and LLMOps, the construction of Retrieval-Augmented Generation (RAG) systems, and the future of production-grade LLM-based applications. Rohit explains that Portkey, a company focused on optimizing the use of large language models (LLMs), addresses the unique challenges of deploying LLMs in production environments, such as cost efficiency and load balancing across multiple LLMs like OpenAI and Azure. The conversation highlights the evolution and importance of semantic caching, which significantly improves response times and reduces costs in enterprise search and customer support. The podcast also explores the implications of cheaper LLM inference on future applications, such as generative feedback loops and orchestration between multiple language models to optimize performance. As LLM inference becomes more cost-effective, the potential for complex decision-making processes and enhanced data storage and retrieval capabilities increases, indicating a shift towards more sophisticated AI-driven solutions.