LLM Observability: What To Instrument and How To Act on It

Improving observability in large language models (LLMs) is essential, as these models can function correctly yet produce inaccurate results. Understanding how to track their decisions is crucial for addressing these issues. The article provides a guide on monitoring errors by tracking the model's inputs, the reasoning behind its decisions, and the outcomes it delivers. Effective observability requires collecting accurate signals, such as monitoring individual request journeys, which aids in identifying logical errors and response delays, ultimately enhancing the performance of intelligent agents in production environments.