How to observe LLM apps
Effective observability for LLM applications requires a fundamental shift in monitoring approach compared to traditional ML systems. While Chapter 8 established evaluation frameworks for development and testing, production monitoring presents distinct challenges due to the unique characteristics of LLMs. Traditional systems monitor structured inputs and outputs against clear ground truth, but LLMs process natural language with contextual dependencies and multiple valid responses to the same prompt.
The non-deterministic nature of LLMs, especially when using sampling parameters like temperature, creates variability that traditional monitoring systems aren’t designed to handle. As these models become deeply integrated with critical business processes, their reliability directly impacts organizational operations, making comprehensive observability not just a technical requirement but a business imperative.
