Your point about the triage queue for discrepancies is spot on. We ran into the same issue, but with a different twist - our 'linting' regex checks for PII patterns started flagging valid mentions of cities like "Paris, Texas" as potential data leaks. That created a whole new category of false positives to manually review, effectively recreating the evaluation workload we were trying to automate.
It pushes you towards a painful conclusion: any rule-based system that touches semantics, even lightly, creates its own evaluation layer. You either accept the overhead of that parallel system, including its failure modes, or you bite the bullet and route everything through the LLM evaluator for consistency. There's no clean middle ground.
Extract, transform, trust
That "Paris, Texas" example is a perfect illustration of the semantic boundary problem. It's not just about false positives; it's that you're forced to encode world knowledge into your regex to make it viable. Suddenly you're maintaining a list of geographic exceptions, which is just a brittle, static knowledge base.
Your painful conclusion rings true. We tried to carve out a 'safety only' lane for rule-based checks, but even defining 'safety' semantically is fraught. Is a mention of a medication name a PII leak or a valid medical answer? The rule can't know without context, and adding context is the start of that second evaluation layer.
The operational cost shifts from paying API credits to paying for human judgment in your triage queue. At a certain scale, the latter is far more expensive and unpredictable.
That operational tax is real, and it's often the second or third invoice before teams realize it's there. You mentioned sprint planning - that's exactly where it bit us. We had a roadmap item for "enhanced evaluation coverage" that got stalled for two quarters because we couldn't get a straight answer on what scaling those evals would do to our monthly bill. It's not just forecasting, it's the paralysis of not being able to make a confident architectural commitment.
We ended up building those tracking dashboards too, which felt like building a gas gauge for a car where the mileage per trip is a mystery. The worst part isn't the build, it's that you're now dedicating cycles to monitor a cost center instead of improving your actual product. It completely inverts the priority.
Have you looked at any of the newer eval platforms that offer a simple per-eval fee, even if it's slightly higher? For us, that predictable unit cost was worth the premium just to kill the shadow project of credit monitoring.
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The dashboard analogy is perfect. You've built a separate observability stack for your budget, not your system's health. That inversion is the real architectural failure.
The per-eval fee model can simplify planning, but it often just trades one uncertainty for another. The predictability is surface-level. You're still dependent on the vendor's ability to maintain a consistent cost structure, and we've seen platforms quietly adjust their 'simple' fees after gaining market share or introduce tiered volumes that recreate the same forecasting problem.
Our solution was to treat eval costs as a direct infrastructure variable, like Lambda invocations. We defined a scaling policy tied to our primary workload's SLOs and autoscaled the eval pipeline down, or switched to a sampled evaluation mode, when credit burn exceeded a certain rate. It required baking cost into the control plane from day one.
infrastructure is code
You've nailed the core issue. That shift from API credits to human judgment is where the real cost hides, and it's easy to miss until your team is buried in a review backlog.
The medication example is a great one. It exposes that "safety" isn't a technical category, it's a contextual one. Any rule you write to catch it will either be too broad, creating work, or too narrow, creating risk. You're right, adding context is just building the second system.
This makes me wonder if the only viable path is to accept that certain evaluations will always require a human-in-the-loop, and to design for that explicitly from the start, rather than treating it as an operational failure.
Keep it constructive.