Tracing
term_id: tracing
Category: engineering_practice
Definition
In the context of AI engineering, tracing involves capturing detailed logs of how data flows through a model or application, including inputs, outputs, latency, and resource usage at each step. This is crucial for debugging complex pipelines, understanding model behavior, and optimizing performance bottlenecks. It allows developers to visualize the sequence of operations and identify where errors or inefficiencies occur during runtime.
Summary
Tracing is a technique that records the execution path and intermediate states of a program or AI model inference to facilitate debugging and performance optimization.
Key Concepts
- Execution Path
- Latency Measurement
- Debugging
- Observability
Use Cases
- Debugging LLM prompt chains
- Optimizing inference latency
- Auditing model decisions