What's the best way to set up alerts for hallucination scores above a threshold?

You've nailed the core idea: routing to a low-priority Slack channel for a burn-in period is essential to avoid crying wolf.

One thing we learned the hard way: you need to make sure that Slack channel is *actively* monitored by someone during that week. If it just becomes ambient noise that no one checks, you'll miss the patterns you're trying to learn. We rotated a "hallucination watch" duty among the team.

Spot on about the p95 check. But that vendor-supplied number becomes gospel way too easily. I've seen teams treat it like a spec limit instead of what it is, a starting point for negotiation.

Your endpoint running at 0.6 is the perfect example. The vendor would have you alerting all the time, and then you just learn to ignore it.

Oh, that's a really good point about teams treating the number like a spec. How do you even start that "negotiation" with the vendor's metric? Do you just tell them their baseline is wrong for your use case? 😅

Yeah, starting with that static 0.4 vendor threshold feels like the natural first step, doesn't it? 😊 I think your rule is a solid place to begin, just so you can see the data flowing.

The "for: 2m" is probably too eager though. You'll get a ton of alerts for single spikes. I'd make that window longer right away, maybe 5 minutes like others said.

My biggest tip would be to start routing alerts to a Slack channel you actually watch during that first week, not to a pager. You need to see what's noise versus a real problem before you decide what the real threshold should be for *your* app.

You've got the right metric and a perfectly reasonable starting point with that 0.4. That Grafana rule will at least get you observing the raw signal, which is step one.

The `for: 2m` is definitely the first thing to change, though. You'll get spammed. Bump it to at least 5 minutes, as others mentioned, to let transient spikes settle. But the bigger shift is moving from that static number to a dynamic baseline. A rule like `traceloop_hallucination_score > (quantile_over_time(0.95, [24h]) + 0.15)` for the same endpoint will start adapting to your actual traffic patterns and model changes.

Also, absolutely route this to a low-priority Slack channel, not a pager, for a week or two. You need to see *what* triggers it before you decide *if* it's critical. Watch for patterns: is it one problematic endpoint? Does it correlate with specific user prompts or times of day? That context is what turns a vendor's generic metric into *your* alert.

Absolutely, that first Grafana rule is crucial just to get the data flowing. I'd call it a smoke test for your monitoring pipeline, not your actual alert logic.

Your suggestion to watch for patterns like a problematic endpoint or specific prompts is the key part. We had a chatbot endpoint that would constantly breach a static threshold because a handful of weird, legacy user queries always triggered weird responses. The static number made us think the whole model was broken. Watching the alerts for a week showed us it was just those specific prompts, which let us fix them in the prompt engineering layer instead of chasing hallucinations.

Your starting rule makes sense to see if data's coming through. I'd probably change the "for" to 5 minutes right away though, to avoid getting spammed by short spikes.

When you say "reduce noise," does that mean you're already seeing alerts on this rule? If so, are they all from one specific endpoint or type of query? That's what I'd try to watch for first.

That's a great question to ask right away. If they're already seeing alerts, understanding the distribution is critical before they even think about tuning thresholds.

I'd push it a step further and suggest they run a quick Splunk query or even a Grafana Explore tab to group those early alerts by `endpoint` and `customer_id` for the last 48 hours. You often find that 80% of the "noise" is coming from a single integration or a specific problematic prompt template that's been deprecated. Treating those as a systemic hallucination problem leads to the wrong fix.

Finally, someone brings up the core issue instead of just tweaking the `for` duration. Your point about empirical correlation is correct in theory, but it's practically impossible for most teams.

You're asking for a "true outcome metric" like user-reported issue rate. For many LLM applications, that signal simply doesn't exist, or it's so sparse and lagged that the correlation analysis is dead on arrival. If I have to wait weeks for enough user reports to correlate against, my model is already in production and causing damage.

The better move is to use a surrogate metric you *can* measure instantly, like the output of a separate, simpler validator LLM call that checks for basic contradiction. It's noisy too, but at least it's contemporaneous. Waiting for the perfect ground truth is how you end up with no guardrails at all.

That's a solid approach with the re-route leading indicator. It forces you to build the painful dataset, but you're right, it moves you upstream.

Our team attempted a similar correlation using session abandonment metrics. The correlation was weaker than we hoped, but it revealed a crucial blind spot: our highest hallucination scores often came from very short, successful-looking sessions where the user got a confident but wrong answer and just left. They never triggered a re-route. So we had to layer that abandonment signal in to catch the silent failures.

The companion alert for request count is a must, but you nailed the fatigue problem. We ended up making it a condition *within* the main hallucination alert rule, not a separate one. Something like `hallucination_score > threshold AND request_count < 0.7 * predicted_volume`. That way it only fires when high scores coincide with a traffic dip, which is the real risk scenario.

Metrics are for aggregates, logs are for details. The second you use a session ID as a label you've lost.

Push the full session context to a structured log with the hallucination score. Keep your metric labels to the few dimensions you'd actually alert on, like endpoint and maybe a coarse error category. Then you can join log queries to metric spikes when you need to debug.

Otherwise you're just building a very expensive, unusable timeseries database.

Exactly right. The cardinal sin here is overloading your metrics with unique high-cardinality labels like session IDs. Your metrics get slow, expensive, and un-queryable.

But the step most teams miss is actually *doing* the join when the alert fires. You need a single dashboard panel or log query template pre-built that takes the firing alert's time range and endpoint label and instantly shows the relevant structured logs. If that join isn't a one-click operation, you'll never do it.

Great starting rule to get alerts flowing. You've already got some good advice on bumping the `for` duration.

One practical thing I'd add: before you tweak the threshold or time window, check if your metric is a gauge or a histogram. If it's a gauge representing the latest score, your rule is fine. But if it's a histogram (like `traceloop_hallucination_score_bucket`), that expression won't work as intended. You'd need to use a function like `histogram_quantile` to calculate the threshold breach.

Also, consider adding a label for the `endpoint` or `deployment` in your annotations if you have multiple services. It'll save you a click when the alert fires.

Your initial rule is a perfectly valid starting point for instrumentation verification. However, treating a hallucination score as a simple threshold is often where teams get stuck in alert fatigue.

The metric type is a critical first check, as mentioned. Assuming it's a gauge, your next step should be to immediately segment the data. Instead of one global alert, create separate rules keyed off a label like `endpoint` or `prompt_template_hash`. You'll likely find the breaches are isolated to specific flows, which points to a prompt engineering fix, not a model-wide issue.

I'd also argue for a companion condition on request volume. An alert for `score > 0.4 AND rate(requests_total[5m]) > 5` ensures you're only notified when the issue is affecting a meaningful number of interactions, filtering out one-off anomalies.

Good start, but you're missing the cardinality trap. If `traceloop_hallucination_score` is a metric with labels for every unique session or user ID, your alert will silently explode your Prometheus costs. Check the metric's actual label dimensions first.

Also, a 2-minute 'for' on a subjective score is pretty chatty. Try 10 minutes and see if the same endpoints keep bubbling up. If they do, you've found a prompt engineering problem, not a monitoring one.

The real trick is adding a volume filter so you only get paged when it's happening on a meaningful scale, like `... AND rate(your_requests_total[5m]) > 10`. Otherwise, expect fatigue by Friday.