LLMs in Production: Key Insights from Our New Report

Large Language Models (LLMs) have captured significant interest, particularly following the launch of OpenAI's ChatGPT in 2022, leading enterprises to explore their potential for competitive advantage. A survey of over 150 professionals across 29 countries revealed that while many organizations are experimenting with LLMs, they face challenges like sharing proprietary data and high costs associated with commercial models. As a result, a growing number of companies prefer using open-source LLMs, which offer more control over data and are cost-effective. Key hurdles in deploying LLMs include data privacy, customization, training costs, hallucinations, and latency issues. The report suggests solutions such as hosting open-source LLMs in private clouds, using configuration-driven tools, and employing techniques like Retrieval-Augmented Generation (RAG) to enhance model reliability. There is a strong push towards building customized LLMs on open-source platforms to maximize the value and operational efficiency of AI investments, with platforms like Predibase emerging to support these needs.