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.