logging

**Logging** in AI and ML systems is the practice of recording **events, data, and system state** for debugging, monitoring, auditing, and improving model performance. Effective logging is essential for understanding what happened, why it happened, and how to fix it. **What to Log in AI Applications** - **Request/Response**: Input prompts (or hashes for privacy), model responses, timestamps, and user identifiers. - **Performance**: Latency (time-to-first-token, total generation time), token counts (input/output), throughput. - **Model Info**: Model version, temperature, max_tokens, and other generation parameters. - **Errors**: Exception details, error codes, stack traces, failed retries. - **Safety**: Content filter activations, refusals, flagged outputs, and the triggering content. - **Infrastructure**: GPU utilization, memory usage, queue depth, instance health. **Logging Best Practices** - **Structured Logging**: Use JSON format with consistent fields rather than free-text messages. This enables programmatic querying and analysis. - **Log Levels**: Use appropriate severity levels — **DEBUG** for development details, **INFO** for normal operations, **WARN** for concerning but non-critical issues, **ERROR** for failures requiring attention. - **Correlation IDs**: Include a unique request ID in every log entry so all events for a single request can be traced across services. - **Avoid Sensitive Data**: Don't log PII, passwords, API keys, or full prompts containing personal information. Use hashing or redaction. - **Sampling**: For high-traffic systems, log a representative sample rather than every request to manage storage costs. **Logging Infrastructure** - **Collection**: **Fluentd**, **Logstash**, **Vector** — collect and forward logs from multiple sources. - **Storage**: **Elasticsearch**, **Loki**, **CloudWatch Logs**, **BigQuery** — searchable, durable log storage. - **Visualization**: **Kibana**, **Grafana**, **Datadog** — dashboards, search, and alerting on log data. - **Analysis**: **OpenTelemetry** — standardized observability data collection framework. **AI-Specific Logging Considerations** - **Prompt Logging**: Log prompts for debugging but consider privacy implications and storage costs for long contexts. - **Output Logging**: Log model outputs for quality analysis, but be mindful of storage (LLM responses can be long). - **Evaluation Logging**: Log human feedback, ratings, and evaluation scores alongside model outputs for continuous improvement. Good logging is the **difference between "something broke" and "we know exactly what broke, why, and how to fix it"** — invest in logging infrastructure early.