How to observe LLM apps
The dynamic nature of real-world operations means that the conditions assessed during offline evaluations hardly cover all potential scenarios that LLMs may encounter in production systems. Thus comes the need for observability in production – a more continuous, real-time observation to capture anomalies that offline tests could not anticipate.
We need to implement monitoring tools to track vital metrics regularly. This includes user activity, response times, traffic volumes, financial expenditures, model behavior patterns, and overall satisfaction with the app. Ongoing surveillance allows for the early detection of anomalies such as data drift or unexpected lapses in capabilities.
Observability allows monitoring behaviors and outcomes as the model interacts with actual input data and users in production. It includes logging, tracking, tracing, and alerting mechanisms to ensure healthy system functioning, performance optimization, and catching...
