Best Practices for Using Large Language Models at Scale

The proliferation of Large Language Models (LLMs) has transformed numerous domains in natural language processing (NLP) and various artificial intelligent (AI)-driven applications. However, efficiently scaling these models involves challenges related to latency, cost, and system complexity. This paper presents a comprehensive set of best practices structured around key areas including direct access to vector databases, direct invocation of OpenAI LLM APIs, optimal scaling of computational resources, reranking of AI search results, dynamic adjustment of context chunk counts, and dynamic model selection to balance cost and quality. It also explores understanding usage modes for cost optimization, leveraging vector caching to reduce embedding expenses, and addressing networking overhead impacts on latency in large-scale generative AI API calls. Together, these guidelines enable scalable, high-performance, and cost-effective LLM deployments in enterprise environments.