What monitoring would you put in place to catch when either the retrieval step or the generation step is becoming a bottleneck in latency during production usage?

To catch latency bottlenecks in retrieval or generation steps, implement monitoring that tracks time spent in each component, error rates, and system resource usage. Start by instrumenting your code to measure the duration of retrieval and generation phases separately. For example, log timestamps before and after each step in your application code, and export these metrics to a monitoring system like Prometheus. Set up dashboards (e.g., in Grafana) to visualize latency percentiles (p50, p90, p99) for both steps over time. This helps identify if one stage consistently takes longer than expected or if latency spikes correlate with specific events, such as traffic surges or code deployments.

Next, monitor error rates and timeouts specific to each step. For retrieval, track failed database queries, API call errors, or cache misses that force slower fallback paths. For generation, log errors like model inference failures or tokenization issues. Use alerts (e.g., via PagerDuty) to notify when error rates exceed a threshold (e.g., 5% of requests) or when latency exceeds a predefined SLA (e.g., 500ms for retrieval). Additionally, track system-level metrics like CPU/GPU utilization, memory pressure, and network latency. For example, if generation latency spikes while GPU usage hits 100%, it suggests the model is compute-bound and may need optimization or hardware scaling.

Finally, implement distributed tracing (e.g., Jaeger or OpenTelemetry) to follow individual requests through retrieval and generation. This helps pinpoint where delays occur—for instance, a specific database query taking too long or a model batch size causing queuing. Use synthetic tests to simulate user traffic and measure baseline performance. For example, run hourly tests that trigger a mix of simple and complex requests, and compare results against historical data to detect regressions. Combining these approaches provides a clear picture of bottlenecks, whether they stem from code inefficiencies, resource constraints, or external dependencies like third-party APIs.