How to trace an LLM failure back to its root cause

Logs are insufficient for diagnosing root causes of failures in large language model (LLM) systems because they capture only the final output without detailing the intermediate steps necessary for understanding where the error occurred. Tracing, as opposed to logging, involves recording the entire execution path—including retrieval, prompt assembly, model calls, and tool arguments—allowing engineers to pinpoint the exact step where the failure originated. Braintrust addresses this by capturing detailed trace trees for each AI call, including metadata about input prompts, model outputs, and configuration details, which facilitates root cause analysis. The process involves reproducing the failure, navigating through the trace tree to identify the failing span, isolating the problematic layer, and confirming the fix with regression tests. This method not only aids in understanding why an incident occurred but also ensures that similar issues are prevented in future deployments by integrating the failing trace into a regression dataset, thereby enhancing the reliability of LLM systems.

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