How to Trace and Evaluate LLM Apps with LangSmith

Building an LLM app is one thing; making it reliable in production is another. LangSmith gives you the tracing and evaluation tools to debug, test, and monitor your AI applications. Here is how to get started.

Step 1: Create a LangSmith Project

Sign up at LangSmith and create a new project. You will receive an API key that connects your application to the tracing backend.

Step 2: Instrument Your Code

Install the LangSmith SDK and initialize it with your API key. If you use LangChain, tracing is enabled automatically. For other frameworks, add the SDK wrapper around your LLM calls to capture traces.

Step 3: Run and Inspect Traces

Execute your application and open the trace view. LangSmith shows every step — the prompt, model call, tokens, latency, and outputs — so you can see exactly what happened and where things went wrong.

Step 4: Create a Dataset

Build a dataset of example inputs and expected outputs from your traces. These become the foundation for automated testing, so you can verify that changes to your prompts or models don’t break behavior.

Step 5: Set Up Evaluations

Create an evaluation run against your dataset. LangSmith scores each output for quality, factual accuracy, or adherence to instructions, giving you a clear picture of performance.

Step 6: Monitor in Production

Keep an eye on the monitoring dashboard for latency, cost, and error rates. Set up alerts so you know immediately if something degrades, and use traces to diagnose issues fast.

Final Thoughts

With LangSmith, you move from guessing why an AI app misbehaves to knowing exactly why. Start with tracing, add datasets and evaluations, and you will ship more reliable AI products.