What is LLM observability? (Tracing, evals, and monitoring explained)

LLM observability is an advanced framework that enhances the management of Large Language Model (LLM) applications by providing insights into system behavior and output quality beyond traditional monitoring metrics like uptime and latency. By tracing each step of a request—from user input to final output—using tools like OpenTelemetry, and evaluating the quality of responses through both offline and online evals, LLM observability allows teams to debug incorrect outputs, track performance variations over time, and manage operational costs more effectively. It addresses common production challenges such as debuggability, reliability, cost control, and safety compliance, enabling teams to identify the root causes of issues and validate improvements before deployment. Braintrust offers an integrated solution for LLM observability, combining tracing, evaluation, and monitoring in a unified platform to ensure reliable, cost-efficient, and safe AI operations. This approach helps organizations like Stripe, Notion, and Dropbox to effectively manage their AI systems, ensuring quality and reliability as usage scales.