LLM instrumentation is crucial for achieving performance and reliability in large language models. LLM tracing helps track down issues such as application latency, token usage, and runtime exceptions, providing detailed insights into the model's behavior. OpenTelemetry (OTel) enhances tracing by offering standardized data collection and integration with various LLM frameworks. However, OTel may not be suitable for all LLM applications, and manual instrumentation or additional frameworks like OpenInference are necessary to properly instrument an LLM app. Automatic instrumentation offers comprehensive coverage but requires less control over the details of what is traced, while manual instrumentation provides flexible control but demands more effort to implement. Various methods for manual instrumentation, such as using decorators, the `with` clause, and starting spans directly, can be employed to customize tracing in LLM applications.