Rolled out TruLens to 20 engineers for chatbot eval - unexpected issues

That's the trap - you build a dashboard that beautifully visualizes the output of your custom evaluator, and you can watch those numbers move for weeks without ever touching a real user problem. The correlation check you mentioned is the only escape hatch.

But even that can be a false positive if you're not careful. We saw a "Helpfulness" score spike correlate with a drop in follow-up queries, but it turned out users were just getting frustrated and leaving, not getting their answer. You need to check correlation with the *right* outcome, not just any metric.

Oh man, the "frustrated and leaving" correlation is a gut punch. We saw something similar with a satisfaction survey pop-up. Completion rate went up, which looked great, but the actual rating tanked. Turned out only the furious users were motivated enough to click the pop-up and vent.

Your point about checking the *right* outcome is huge. It's easy to grab the nearest metric that moves and call it a win. We started pairing every automated score with a simple "did the conversation stop?" check after 48 hours of inactivity. If a score jump aligns with conversations dying, not resolving, you've probably built a user annoyance detector, not a quality metric.

And here's the first trap. You've already moved from evaluating your chatbot to managing your team's consensus on what "actionability" even means. Good luck getting 20 engineers to agree on a concrete, executable step without a two hour meeting.

That "single source of truth" complexity isn't an operational hurdle, it's the core failure. You traded the messy, valuable signal of actual user outcomes for the clean, meaningless signal of an internally-blessed rubric. Now you're just measuring how well you follow your own rules, not if the bot helps anyone.

Defining those custom evaluators is the right first step, but I've found their real test is in calibration, not creation. Your **Actionability** metric is a perfect example, it's easy for two engineers to have wildly different interpretations of what constitutes a "concrete step."

We had the same issue and solved it with a quick, ugly calibration set. We took 50 real responses and had 5 team members independently tag them on a simple binary: "Would a user know what to do next? Yes/No." The disagreements were the most valuable part, they forced us to write a concrete rubric with examples before a single line of TruLens evaluator code was finalized.

Without that step, you're just scaling subjective opinion.

You've hit on the crucial step that's so easy to skip. That "ugly calibration set" is the difference between measuring something and measuring nothing.

We've started calling those disagreements "rubric gold." When two engineers mark the same answer differently, you've found the exact edge case that needs to be defined in your evaluator prompt. Documenting why each person voted the way they did is often more valuable than the final rubric itself. It exposes unspoken assumptions.

The only caveat I'd add is that you need to include some non-technical folks in that calibration group, or at least review their real user feedback. Engineers can have blind spots for what's "obvious" to an end-user. What feels like a concrete step to us might be jargon soup to them.

That complexity around managing a "single source of truth" for evaluators is the real product, not the evaluation scores. You're building an ontology. It's less a technical rollout and more a change management exercise for 20 people.

The specific point about **Contextual Adherence** versus **Actionability** is a great example. One is a technical guardrail for RAG, the other is a user-centric quality metric. They'll pull your team in opposite directions if you don't anchor them both to a shared outcome, like a reduction in user escalations. The evaluator definitions become a battleground without that shared north star.

How are you handling conflict resolution when two engineers, looking at the same bot response, disagree on its score for one of these custom dimensions? That's where the process will either solidify or collapse.

You're right that the ontology becomes the product, but you're missing the cost ledger that underpins that change management exercise. Every disagreement on a "Contextual Adherence" vs "Actionability" score isn't just a process question, it's a direct cost variance.

If one engineer's interpretation requires a more expensive LLM call to a deeper context window for the evaluator itself, and another's doesn't, you've just introduced a financial variable into your scoring rubric. We had to start tagging each evaluator definition with its average per-evaluation cost, because the "purest" version of a metric was sometimes 10x more expensive to compute than a slightly less nuanced one. The battle over definitions is often a proxy battle over whose architectural choices get budget authority.

This is a really sharp point that I hadn't considered at all. It makes the whole evaluator debate feel much more concrete, and frankly, a bit scary for budgeting.

You mentioned tagging definitions with their per-evaluation cost. Do you have a standard process for calculating that? I'm imagining it's not just the LLM call, but also the compute for any pre-processing logic. Do you track it in a shared doc, or is it automated?

Because if it's a manual spreadsheet, that feels like another source of drift, right? The cost of a "pure" metric could quietly creep up as the evaluator prompt gets tweaked.

Great question. We automated it because the manual spreadsheet was, as you guessed, a nightmare for drift. We built a simple wrapper that logs token usage and execution time for each evaluator run against a small, fixed sample set (like 100 canned responses). That runs nightly in a pipeline.

The tricky part isn't the LLM call cost, it's the pre-processing. One of our "Actionability" evaluators started doing extra sentiment analysis as a precondition, and the cost silently tripled. Now our dashboard shows a per-evaluation cost trend line next to the metric definition. It's the only way to keep the "pure vs. practical" debate honest.

But it creates its own meta-problem: you start optimizing for cheap metrics, not good ones. Gotta watch that.

You're absolutely right to start by defining custom evaluators. A lot of teams skip that and just use the defaults, which often don't map to their actual business goals.

The two you've highlighted, Contextual Adherence and Actionability, are a classic pairing that often work at cross-purposes without careful calibration. A response can be perfectly adherent to the provided context yet completely lack actionable guidance, and vice versa. I'm curious, have you seen any tension between these two scores in your initial runs? It's a common early signal that your evaluation ontology might need a hierarchy, where one dimension can veto another.

The versioned API is the only real solution. We tried git tags and commit hashes first, but someone would always force-push or rebase and break the link. That overhead you added is the price of keeping your historical data sane.

Now the problem is your engineers arguing over which version to pin the dashboard to, because everyone wants their new "improved" evaluator to be the default.

That's a really interesting breakdown, especially the point about custom evaluators. I'm just starting to look at TruLens for a smaller team, so this is helpful caution.

You mentioned **Contextual Adherence** and **Actionability** as custom metrics. I'm curious, when you set those up initially, did you base them on existing user support tickets or internal guidelines? Or was it more of a team brainstorm on what "good" looks like?

Asking because I'm trying to figure out how to anchor our own definitions to something real, to avoid starting with purely theoretical goals.

You're right to be skeptical of the score itself. The trap is thinking a 0.92 has intrinsic meaning. It doesn't. The value is in forcing the debate that created the rubric behind the number.

We anchored our "Actionability" to a simple, brutal metric: did the support ticket get closed after the user got the bot's answer? We sampled hundreds of resolved tickets, graded the bot's suggested answer, and built the rubric from that. The score is still a proxy, but it's a proxy for a real business outcome, not an engineer's opinion of "helpfulness."

The real test is whether a low score triggers the same action a real user complaint would. If not, you've just built a very expensive dashboard.

Managing a "single source of truth" for evaluation criteria is indeed the central challenge, and it scales non-linearly with team size. You've identified the correct friction point.

My recommendation is to treat your evaluator definitions not as a static artifact, but as a configuration layer with its own CI/CD. For a team of 20, you need a formal change log, versioned definitions, and a required peer review step for any new evaluator or modification. This prevents the gradual fragmentation of understanding, where engineers in different sprints are implicitly scoring against different rubrics.

The two custom evaluators you're starting with, Contextual Adherence and Actionability, will create immediate tension in your results. You'll see high-adherence, low-actionability responses, which forces the conversation about priority. This is actually a good early stress test. It compels you to decide if adherence is a binary gatekeeper for actionability, or if they are weighted independently. Without that architectural decision made explicit upfront, your model selection signals will be contradictory.

Exactly! That's the permanent step-change problem in a nutshell. We hit that head-on and realized you can't have it both ways: either you re-run your entire historical dataset with the new evaluator (massive compute cost, and what if you no longer have the raw model outputs?), or you accept that your dashboard's trend line is now comparing apples to oranges.

Our compromise was to always display the data with a clear visual break at the evaluator version change, like a dotted vertical line on the chart. The key was adding a toggle so you can view "All History (with version breaks)" or "Current Evaluator Only," which re-runs the latest metric on a rolling window of recent data. It's not perfect, but it keeps the team honest about comparing scores across different definitions.

Honestly, that visual break was a great forcing function. It made everyone pause and ask, "Is this score jump because the bot actually got better, or just because we changed the goalposts?"