LLM tracing: The complete guide

Traditional logging methods fall short for Large Language Model (LLM) applications because they are designed for deterministic systems, where a single log line can reveal sufficient information about an event or outcome. In contrast, LLM applications involve complex and non-deterministic processes, often requiring multiple steps such as retrieval, tool calls, and model interactions, which makes it difficult to trace errors or unexpected outputs through logs alone. LLM tracing provides a complete, structured record of a request by breaking it into interconnected spans, each representing a distinct operation, thus offering a clearer picture of the execution path and the ability to identify where errors or incomplete context occurred. This tracing approach is essential for debugging and optimizing LLM applications, as it captures detailed information about each step, including inputs, outputs, timing, and token usage, which is crucial for understanding the "why" behind a response, not just the "what." Braintrust offers a comprehensive solution for LLM tracing, enabling teams to incorporate tracing into their workflows seamlessly, leveraging both SDK and OpenTelemetry integrations, and providing tools for online scoring, monitoring, and regression testing to ensure quality and efficiency in LLM applications.

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