langsmith-observability

Debug incorrect LLM responses by using tracing to capture detailed logs of LLM calls, prompts, and responses. This lets you trace conversation flows and identify the specific prompt or model call causing the issue.

Yes, you can evaluate LLM outputs against datasets using built-in or custom metrics. This systematic testing helps you verify model outputs and build reliable evaluation pipelines for your AI applications.

Yes, LangSmith integrates with OpenAI, Anthropic, LangChain, and LlamaIndex. This allows you to capture traces, evaluate outputs, and monitor production systems across these different LLM frameworks.

The best way to monitor production LLM systems is by tracking performance, errors, and costs systematically. This observability ensures your AI applications run reliably and helps pinpoint production issues quickly.

Build a systematic testing pipeline by creating evaluation datasets to test fixes for your LLM applications. You capture traces of problematic model calls, generate datasets from them, and apply custom metrics to evaluate outputs.