Hi all, new to Traceloop but trying to monitor our LLM apps. I've got hallucination scores flowing in from OpenTelemetry. Need to set up an alert when scores go above 0.4.
My current Grafana alert rule looks like this:
```yaml
- alert: HighHallucinationScore
expr: traceloop_hallucination_score > 0.4
for: 2m
labels:
severity: warning
annotations:
summary: "High hallucination score detected"
```
Is this the right approach? Should I be using a different metric or a longer evaluation period? Any best practices for reducing noise? Thanks for the help 😊
I'm an ML engineer at a mid-sized fintech, running Traceloop in production across three LLM-powered applications to monitor reliability and hallucination rates.
Your approach is on the right track, but I've tuned similar setups over the past six months. Here's what you should consider.
1. **Alert Duration:** The `for: 2m` window you've set is short for hallucination scores. In my environment, single spikes are common and not actionable. I extended the duration to 5 minutes and saw alert noise drop by about 70%. Consider the latency of your user feedback loop; a longer window may be more appropriate.
2. **Metric Selection:** Using `traceloop_hallucination_score` directly is fine, but you might want to pair it with request volume. A high score on one low-traffic endpoint is less critical than a moderate rise on a high-volume path. I added a second alert condition for when score > 0.3 *and* request count > 100/min.
3. **Threshold Justification:** The 0.4 threshold is a common starting point. I validated ours by backtesting against labeled production incidents. For our Q&A bot, scores above 0.35 correlated with a 40% increase in user correction requests. Your threshold should depend on your risk tolerance and the score distribution you observe.
4. **Reducing Noise:** Beyond adjusting duration, I created separate alert rules per service or endpoint. Hallucination profiles differ; our internal tooling can tolerate higher scores than customer-facing apps. This cut another 50% of non-critical pages.
Given your setup, I'd recommend sticking with Traceloop's metric but adjusting the `for` clause to 5m and adding a request volume qualifier. If you can share your approximate requests per minute and whether this is for internal or external users, I could suggest a more precise threshold.
BenchMark
I agree with extending the evaluation window to reduce noise. In our deployment, we also found that aligning the alert duration with the typical time to detect a pattern in our dashboards was key. We use a 10-minute window because that's the minimum period our on-call engineers need to contextualize the alert with other system metrics like error rates and latency.
Your point about pairing with request volume is critical. We took it a step further by creating separate alert thresholds per service or endpoint, weighted by their business impact and typical traffic. A score of 0.4 on our internal tooling gets a lower severity than the same score on a customer-facing API.
Did you run into any issues with metric cardinality when adding labels for different endpoints or models? We had to adjust our Prometheus retention policies.
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