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

Hey, I've been setting up similar monitoring and your rule looks syntactically correct. The main thing I'd question is that 2-minute evaluation window. In my experience with LLM apps, scores can jump around quite a bit between individual requests even when the overall system health is fine. I've seen a 5-minute window catch real sustained problems while filtering out most of the one-off weird generations.

Have you looked at the distribution of scores for your specific endpoints yet? I ask because we found that 0.4 was actually below the normal operating range for one of our more creative workflows, which would have meant constant alerts. It might be worth checking the p95 for each endpoint before locking in that global threshold.

I also saw endpoint-specific behavior where the p95 was our new baseline. For our creative writing endpoint, the p95 hallucination score sits around 0.6 during normal operation, so a 0.4 alert would have been useless noise.

Your point about a 5-minute window is solid. We found that duration, combined with a percentile threshold per workflow (like p95 + 0.1), filtered out transient spikes while still catching genuine degradation.

Your rule's syntax is correct, but the underlying statistical assumptions need validation. The core issue is that you're applying a universal threshold to a vendor metric without establishing its statistical validity for your specific data generation process. This is a common Type III error - solving the wrong problem precisely.

Before implementing any alert, you must perform an empirical correlation analysis. The paper "Monitoring with Meaning: Establishing Statistical Rigor for Business Metrics" (Brodersen et al., 2021) details the methodology. For your case, you need to:

1. Gather a time-series dataset pairing your `traceloop_hallucination_score` with a true outcome metric (e.g., user-reported issue rate, session abort rate) for the same endpoint and time granularity.
2. Calculate the Pearson and Spearman correlation coefficients. A coefficient below 0.3 suggests a weak relationship, making the alert a likely source of operational noise.
3. If correlation exists, use change-point detection (like PELT) on the hallucination score series to identify a data-driven threshold where the outcome metric deteriorates, rather than the arbitrary 0.4.

The `for: 2m` duration is almost certainly insufficient given the stochastic nature of LLM outputs. You need to analyze the autocorrelation function of your metric to determine an appropriate window that separates signal from inherent model variance. A 10-minute window is a starting heuristic, but the correct value is data-dependent.

Have you performed this validation to ensure the metric has predictive value for your application's reliability?

You're right about the correlation analysis being a prerequisite, but that paper's methodology is overkill for getting an alert out the door. The real trap is treating this like a research problem instead of an operational one.

I ran those correlation checks for our endpoints and found something more useful than a coefficient: the actual threshold where users start hitting the "report issue" button is a moving target that changes weekly as the model updates. So even a perfect correlation today can be noise next month.

Instead of chasing statistical purity, we just alert on the 90th percentile of the score over a 4-hour sliding window, per endpoint. If today's traffic is consistently worse than the recent norm, that's the signal. It's not elegant, but it's stopped paging us for vendor drift while still catching real regressions.

Your rule will fire, but it'll likely be noisy. The 2-minute window is the main culprit - LLM scores can fluctuate heavily between individual requests. I'd start with at least a 5-minute `for` clause.

The 0.4 threshold is a bigger unknown. Before you commit, check your historical data for the p95 score per endpoint. We found one endpoint normally runs at 0.6, so a 0.4 alert would have been useless. An endpoint-specific baseline is more reliable than a global magic number.

Great question, and you've got some solid advice already about the `for: 2m` being too short - absolutely agree. The main thing I'd add is about that static `0.4` threshold.

You need to treat your model like a feature that's always evolving. A threshold that works today can become meaningless after a model update or a shift in user prompts. Instead of hunting for a perfect static number, consider setting a baseline relative to your own traffic.

For each endpoint, calculate the rolling 24-hour p95 score. Alert when the current 10-minute average breaches, say, that baseline + 0.15. This way you're catching actual degradation relative to your app's recent behavior, not just an arbitrary vendor-defined line. Saved us from a ton of noise last quarter.

Welcome! Your rule is definitely on the right track - I used a nearly identical one when I first set up Traceloop monitoring. The `for: 2m` seems to be the common point of friction everyone's mentioning, and I have to agree. I'd echo pushing that to at least 5 minutes to smooth out the noise.

The part I'd add to the discussion is about alert destinations. That `severity: warning` label is perfect for routing. Have you considered sending these alerts to a Slack channel or a low-priority ticketing system first, instead of straight to a pager? We found that gave us a chance to review patterns without waking anyone up at 2 AM for what might just be a temporary spike. It helped us tune the threshold and duration before we promoted it to a critical alert.

Good point about the alert destinations. Routing warnings to Slack first is a smart way to build intuition.

One caveat from our experience: if you have a high volume of warnings, that Slack channel can become noise and get muted. We found it helpful to pair it with a simple dashboard showing the count of those warnings over time. That way, a human can glance and see if it's just the usual background hum or a creeping trend worth investigating, without being pinged for each one.

Absolutely, checking the p95 per endpoint is the right first step. We made that same discovery early on.

But we found that even a per-endpoint static number can drift over time as user behavior changes. After a big product launch, the mix of prompts hitting our main endpoint shifted enough that the p95 crept up by 0.2. The old alert stopped firing, even though quality felt worse.

So now we treat that p95 check as a starting point, then layer in a rolling baseline like user927 mentioned. It's a bit more work to set up, but it adapts.

That's a solid start, and your rule will definitely work. The biggest thing I'd change right away is that `for: 2m` - I'd bump it to 5 minutes at least to avoid alerting on brief spikes.

The `0.4` threshold is the real puzzle. It's tempting to just pick a number, but it really depends on your specific endpoints. Like others said, check the p95 for each one. We have a support bot endpoint that normally runs at 0.1, so 0.4 would be a five-alarm fire, but our creative copy tool lives around 0.55. A global threshold would miss one or cry wolf on the other.

Maybe start by routing those warnings to a dedicated Slack channel instead of a pager, so you can watch the patterns for a week before locking in the numbers.

Finally, someone talking about real production data instead of statistical purity.

Backtesting thresholds against actual incidents is the only way to sleep at night. I did the same with user refunds on our support bot. That 0.4 vendor number meant nothing, but a 0.31 in our logs? That's when the chargebacks started.

Your point about pairing with request volume is good, but be careful. That's another moving baseline. If your high-volume endpoint suddenly drops traffic because of a hidden problem, your alert won't fire. Seen it happen.

Exactly. Backtesting against incidents is the only method that's ever given us a defensible threshold.

But your refund example highlights the real trouble: you're correlating to a lagging indicator. By the time chargebacks hit, the damage is done. We had to find leading indicators, like a spike in user session re-routes to human support within five minutes of a high hallucination score. That's a more painful dataset to build, but it moves the alert upstream.

The traffic volume blind spot is real too. We added a companion alert for request count deviation, but it's tricky to tune without creating alert fatigue.

The real question is why you're trusting a vendor's hallucination score in the first place. That 0.4 is a black box number designed to sell you monitoring, not reflect your actual quality issues.

Set your threshold based on when *your* users complain or refunds spike, not when Traceloop's metric blinks.

Your for:2m is too short. Bump it to 5 minutes minimum to avoid alerting on transient spikes.

Ignore the static 0.4. Calculate the 95th percentile for each of your endpoints over the last 24 hours. Alert when you exceed that baseline by a fixed margin, like +0.15. It adapts to model updates and traffic shifts.

Start by routing to a low-priority Slack channel, not a pager. Watch it for a week to see patterns before you lock anything in as critical.

The per-endpoint p95 plus a fixed margin is the practical move, but you're still vulnerable to a quiet degradation. If quality slowly erodes from 0.2 to 0.34 over a month, your +0.15 alert never fires because the baseline crawled with it. You need something to occasionally check that your new normal isn't actually garbage.