LangSmith

We started using LangSmith while building an internal assistant that combines retrieval with several model and tool calls. Before that, debugging usually meant checking application logs, model outputs, and retrieved content in different places. LangSmith made the process much easier because we could view the full run in one trace. The feature I use most is the ability to inspect each step and identify where a weak answer started. In quite a few cases, the model was not the real problem. The issue was poor retrieval, missing context, or a tool returning an unexpected result. Being able to see that quickly has saved the team a lot of back-and-forth. We also use a small evaluation set when changing prompts or models. It does not replace human review, but it gives us a more consistent way to compare versions and has helped us catch issues before releasing changes. My experience with LangSmith has been positive. It has made debugging faster and conversations about quality less subjective. It is most valuable for teams that are prepared to define what a good answer looks like for their own application.