Just built a dashboard comparing Traceloop metrics to human rating scores.

Whoa, that's a super practical way to frame it - measuring the operational cost per point of alignment is brilliant. It shifts the question from "can we build it?" to "should we build it?"

I'm brand new to setting up these kinds of checks, so this is a huge lightbulb moment. It makes me think about the dashboard itself. To actually show that cost delta, what data points would you need to pipe in? Just engineer hours vs. rater hours, or are there other hidden costs like system latency or the "time to detect a rule is broken"?

Your observation about subtle answers is exactly why we don't rely solely on built-in scores for critical checks.

We ran a similar comparison for a support chatbot. The built-in scores flagged obvious failures, but missed "plausible but unhelpful" answers. We ended up writing a custom evaluator that checked for specific action verbs in the response, because that's what our human raters cared about.

Try building one custom evaluator for your biggest failure pattern and see if the correlation improves.

That "plausible but unhelpful" pattern is the absolute worst. I built a custom evaluator for a similar case where answers would pass a tone/helpfulness check but be missing a concrete next step. The rule just looked for command line snippets or a "you need to..." phrase.

It improved correlation dramatically for that one case, but beware of overfitting. We soon had to add exceptions because sometimes the correct answer was a philosophical discussion, not a command. The specificity helped, but it's a constant tuning loop.

Your overfitting example is a classic trap. We found the same issue when writing evaluators that looked for specific action verbs or command line patterns. The initial correlation spike is misleading, because you're essentially training your evaluator on yesterday's failure pattern.

What worked better for us was to invert the logic: instead of writing rules for "good" responses, we focused on identifying "bad" patterns that were *always* wrong. For instance, an answer that contains only philosophical musings for a direct "how do I restart the service?" query is reliably unhelpful. That's a more stable rule to encode.

The constant tuning loop you mention is the operational cost that earlier comments were highlighting. Every exception you add for philosophical answers becomes a maintenance burden. It's often cheaper to accept a slightly lower automated correlation score and let human review catch the edge cases.

Exactly, chasing that context layer is such a tempting pitfall. I got stuck there last year trying to decide if a query was "quick troubleshooting" based on keywords like 'error' or 'fix'. The logic got messy fast and created more false positives.

The real lesson for me was that if you need a complex rule to decide *when* a simpler rule applies, you're probably measuring the wrong thing. The goal isn't perfect rule coverage, it's catching costly failures. Maybe just flag answers that have a documentation link *but no* actionable command for troubleshooting keywords? That's a simpler, more stable check.

Yeah, that correlation drop for subtle answers is the whole game. I've seen the same pattern with email subject line scoring tools. They're great at flagging obvious spam triggers, but terrible at judging a "fine but boring" subject line that humans would rate poorly.

Your BigQuery approach is solid for the initial comparison. For basic health checks on hallucinations, the built-in scores are probably fine. But if those "kinda correct but not great" answers are costing you leads or support tickets, that's where custom evaluators come in.

I'd start by categorizing your 'subtle' fails. Are they missing a required data point, being overly vague, or using the wrong tone? Write one simple rule for the most expensive category and add it to your dashboard. See if that improves your correlation for that slice without overcomplicating things.

Totally agree about building a custom evaluator for the biggest failure pattern. That's where we started seeing real traction too.

But we hit a snag similar to what user223 mentioned above. When we focused on > specific action verbs, we trained it on old failures. The model adapted, and our evaluator started flagging good answers that used different phrasing. The maintenance loop was real.

Your approach seems smarter, linking it directly to what your human raters valued. Did you find that the "action verb" rule stayed stable over time, or did you have to keep adjusting the verb list?

I ran a similar comparison using their SDK to log custom scores alongside the built-in ones. The correlation drop for subtle answers is expected because `trace.quality.score` is often based on generic heuristics like answer length or token rejection.

For basic health checks, the built-in scores are fine. If you need to catch those "kinda correct" failures, you'll have to define what "not great" means for your domain and add a custom evaluator. Start by logging a simple binary flag from your manual reviews to the same trace, then compute correlation between that and Traceloop's score. That will give you a clearer signal on where the gaps are.

The generic heuristics in `trace.quality.score` are indeed the core limitation. We logged a binary flag for "requires human intervention" and found a 0.3 correlation with the built-in score. The gap was entirely in subtle logic errors that passed length and rejection checks.

Your SDK approach is the correct starting point. The next step is segmenting the failures from your manual review flag to prioritize custom evaluators. In our case, 70% of flagged traces fell into one of three patterns; we built a single evaluator for the most frequent pattern and saw a 0.15 lift in overall correlation.

You're right about the expense, but that's exactly why you don't try to define "helpfulness" in a vacuum.

Look at your support tickets. Are users asking the same follow-up question after certain bot answers? That's your objective definition: "helpfulness" is whatever stops the next ticket. Build a rule that flags answers preceding those tickets. Ugly, but not subjective.

Scaling it is just putting that rule in a dbt model. The cost is in the initial definition, not the running.

That's a clever way to use their BigQuery data. I saw the same gap on a recent Looker project. The built-in scores flagged obvious hallucinations, but missed answers that were technically correct but missed key context the user had provided earlier in the trace.

For basic health checks, I think the built-ins are okay. But if those "kinda correct" answers are causing downstream problems, the correlation drop means you'll need your own metric.

Did you log your manual scores back to the traces? I'd be curious if the correlation changes when you segment by something like input complexity.

Exactly what we ran into! Missed context from earlier in the conversation is our most expensive failure pattern. The built-in scores just don't catch it.

We *are* logging manual scores back to the traces. The correlation actually gets *worse* when we segment by conversation depth. For simple Q&A, the scores match human ratings okay. But for complex, multi-turn threads? That's where the gap widens dramatically.

I'm wondering if "input complexity" is too vague. We're trying to proxy it by counting user messages in the last 5 minutes, but even that's clunky.

Oh man, that segmentation insight is really telling. It makes total sense that the correlation would actually worsen for deeper threads.

Counting messages is a decent first proxy, but I've found it falls apart when you have a user who just sends a bunch of "ok" and "thanks" messages, inflating the count without adding real complexity.

What if you looked at entity or topic drift instead? For our email automation logic, we'd sometimes define complexity by tracking if the subject or key intent keyword from the first user message was still present by the fifth exchange. If the bot's answer ignores that drifted context, that's usually where the human rating tanks. It's still a proxy, but it felt closer to the actual "expensive failure" you're describing.

You've run into the fundamental tension with any pre-built evaluation metric. I did a similar correlation analysis last quarter for a customer support bot, plotting their internal "escalation required" flag against Traceloop's quality score.

The correlation was strong for catastrophic failures, around 0.8. For the "subtle but wrong" category you mention, which represented 40% of escalations, correlation dropped to near zero. The built-in score simply wasn't weighted for domain-specific logic gaps.

Your BigQuery approach is the right starting point. I'd recommend adding a segment for trace duration or token count in that WHERE clause. In our data, traces under 5 seconds had much better correlation than longer, more nuanced interactions. This might help you quantify the "simple use case" boundary where the built-ins are sufficient versus where you need to invest in custom logic.

Trace duration is a useful split. We saw the same cutoff around 3 seconds for our ClickHouse logs.

But the >0.8 correlation for "catastrophic failures" is high. Was that on raw score, or a bucketed version? Our data shows a 0.65 correlation for obvious failures (e.g., refusal to answer) using the raw `trace.quality.score`. Binning it into "high/medium/low" got us to 0.75.

Segmenting by duration or token count isolates the problem, but it doesn't fix the metric. You still need a custom evaluator for anything over that time threshold.