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

Your finding about correlation dropping on subtle outputs is the key takeaway. Automated scores are proxies, not replacements. For basic health checks on clear failures, they're fine. But if "kinda correct but not great" matters for your use case, you'll need custom evaluators targeting those specific failure modes. I'd be interested to see if the correlation improves when you break your human rating into separate dimensions like factual accuracy vs. completeness vs. helpfulness, and check each against Traceloop's sub-scores.

That's a good idea about breaking the rating down. Traceloop's quality score is a single number, so it's probably averaging things we'd separate.

But how do you objectively define dimensions like "helpfulness" for a custom evaluator? Seems subjective and expensive to scale.

"Objectively define" is the wrong goal for a custom evaluator. You can't fully automate a subjective judgment, you can only approximate it with a set of rules your team agrees on. The expense is upfront in designing those rules.

For "helpfulness," you'd start by listing clear, observable failure patterns you care about. Does it answer the specific question asked? Does it include unnecessary fluff? Does it omit critical steps? Then you write code to detect those patterns. It's brittle, but that's the trade-off.

Yes, scaling human ratings is expensive. That's why you only use custom evaluators on the metrics that directly impact your business logic, not every possible dimension. Use Traceloop's broad score for overall drift, and your own rules for the specific failures that cost you money.

You've hit on the core tension. User31 is right about starting with observable failure patterns, not philosophical definitions.

For a practical example, we built a "helpfulness" proxy for a support bot by checking if the final answer contained any of the key entities from the user's question. If the question asked about "error code 550" and the response never mentioned "550", that's a clear, rule-based failure we could log. It's not perfect helpfulness, but it's an objective signal that correlates highly with human ratings in our case.

The scaling cost isn't just in the rules, but in maintaining them as your product changes. That's why you pair it with Traceloop's generic score to catch drift you haven't codified yet.

This is such a practical example, thank you. That approach of checking for key entities is smart because it turns a fuzzy concept into a verifiable, binary condition. It reminds me of how we sometimes use a "topic adherence" check in similar situations.

The maintenance cost you mentioned is real. We've found that those simple rule-based signals can become noisy over time as the product language evolves, unless someone is actively curating the list of key terms. It's a trade-off between a stable, broad metric and a precise, high-maintenance one.

Pairing them, as you suggest, seems like the only sustainable path.

Exactly. The "active curation" part is where many teams underestimate the commitment. It's not just a one-time list of terms. It becomes part of the product feedback loop. When support flags a new recurring issue, that new term or error code needs to be added to the evaluator's checklist.

That's why pairing with a vendor metric like Traceloop's is so valuable. It gives you a stable baseline while your custom rules are in flux. You can watch for divergence between the two scores, which often signals it's time to review and update your own rule set.

Yeah, that makes sense about starting with failure patterns. I was getting stuck trying to define "good" in the abstract.

But how do you actually get that initial list of patterns? Is it just from looking at a bunch of past failed conversations and manually spotting common threads? That seems like it could take forever if you're just starting out and don't have a huge history of failures yet.

Looking at past failures is one way, but it's a slow start. You don't need a huge history. Start with your acceptance criteria from the vendor contract or your own internal spec.

What were the minimum viable requirements you signed off on when you bought or built this thing? Those are your first failure patterns. If the spec says "must provide a troubleshooting step for error code X," and it doesn't, that's your first rule.

This flips the script from searching for what's wrong to verifying what you were promised. It's faster and ties directly to whether you're getting what you paid for. The vendor's own documentation often lists the failure modes they claim to handle.

That's a really clever way to frame it, using the vendor contract or spec as the starting point. It turns a compliance check into a functional test.

A caveat I've seen is that specs can sometimes be vague on the actual output format. For example, a requirement might say "provide a step-by-step guide," but doesn't specify if bullet points are required or if a paragraph is acceptable. That can make the rule-based check a bit fuzzy at first. Did you run into that, where you had to tighten the spec's language before you could codify the rule?

Absolutely, that's the exact challenge. Starting from the contract or spec doesn't eliminate ambiguity, it just brings it to the surface earlier, which is its own benefit. You're right that "provide a step-by-step guide" is un-codifiable.

My process is to treat the initial vague spec as a prompt for the first round of human review. We'd run a batch of responses against that requirement, have a human label them as pass/fail, and then analyze *why* they failed. That analysis creates the concrete, codifiable rules.

For your example, we might find that paragraph-formatted steps were consistently rated unhelpful because users missed critical actions buried in the text. That objective finding lets us go back to amend the requirement to "provide a step-by-step guide using a numbered list" and codify a rule checking for numbered items. The spec tightening and rule creation happen in the same iterative loop.

It does add a step, but it turns a subjective debate about formatting into a data-driven decision based on user outcomes.

That's a solid process, and it aligns closely with how I've seen effective governance teams work. The key step is using that initial human review not just to label, but to uncover the operational reason for failure.

One caveat I'd add is that sometimes the data from that first review reveals the spec itself is wrong. You might find users *prefer* the paragraph in certain, more narrative contexts. So the rule becomes conditional, not just a blanket "must use a numbered list." It pushes you toward a more nuanced evaluator that considers context, which is harder to build but often more accurate.

Totally agree about the spec sometimes being wrong. We had a similar case where our "must include a link to the relevant documentation" rule backfired. In quick troubleshooting contexts, users found the links distracting and just wanted the direct command to run. The rule was technically satisfied, but hurt the rating.

That conditional nuance is the killer. How do you decide when the paragraph is okay vs when it's not? Is it based on query length, or keywords, or something else? Building that context layer feels like the next mountain to climb after you get the basic rules working.

You're hitting on the core problem with automated checks - they optimize for the rule, not the outcome.

Your "must include a link" rule is a perfect example. It's a proxy for completeness, but a bad one. The actual requirement is probably something like "provide sufficient guidance for the user to solve the problem." A link is one way, a direct command is another. The metric should measure the result, not the method.

Instead of trying to build a complex rule engine to decide between paragraphs and lists, you should ask what you're *really* trying to measure. Is it clarity? Speed of resolution? That's what your human rating scores are for. If the dashboard shows the automated rule passing but human scores dropping, the rule is wrong. Scrap it.

Chasing that "conditional nuance" is how you end up with an over-engineered evaluation system that costs more to maintain than the AI itself.

That's a critical distinction, and it exposes the real economic trade-off. You're describing the cost of chasing fidelity. The moment you start adding context layers to your rules, your engineering maintenance costs increase non-linearly. You need a dedicated team to tune and validate those conditional branches, and that's a permanent operational expense.

So the financial question becomes: what's the delta between the cost of that engineering team and the cost of the human raters you're trying to replace? If the rater cost is lower, you've built a net-negative system. The dashboard's value is in quantifying that delta. It should show not just a correlation score, but the operational cost per percentage point of alignment you gain by adding another rule.