How to Capture and Review LLM Traces for Reliable Evaluation
Explore how to reliably capture end-to-end large language model traces including user inputs, system prompts, and intermediate steps. Learn best practices for storing immutable structured data and designing custom annotation tools that simplify reviewing traces. Gain skills to build a minimum viable evaluation setup for systematic error analysis and reliable LLM evaluation workflows.
The previous lesson focused on interpreting system behavior through traces and error analysis. That work assumes access to meaningful traces. In practice, this is often the first point of failure. Teams collect incomplete logs, lose intermediate steps, or lack sufficiently diverse examples to learn from. Without a reliable way to generate and capture traces, even well-designed evaluation frameworks stall.
This lesson focuses on deliberately producing traces. The first part covers how to capture complete, end-to-end traces from the system and review them efficiently using custom workflows and tools. The second part focuses on generating inputs, including synthetic data, that force the system to exercise different behaviors. By the end of the lesson, readers should know how to reliably create and work with the traces needed for error analysis and evaluation at scale.
How do I capture traces from my system?
As we have discussed, to evaluate a large language model (LLM) system meaningfully, you need more than outputs. You need a complete record of how each response was produced. Capturing traces means designing the system so that every user interaction can be inspected end-to-end. This is not primarily a tooling problem. It is a workflow and instrumentation problem.
At a minimum, every user interaction should correspond to a single trace. That trace should include:
The user input
Any system or developer prompts
All model calls
Retrieved context
Tool invocations and their results
The final response returned to the user
If any of these steps are missing, the evaluation becomes unreliable. You may know something went wrong, but you will not know where or why.
A common mistake is relying on partial logs, such as logging only the final prompt or the final output, rather than the entire log. This creates the ...