---
title: "Comment by Yunus Emre Vurgun on \"Routing AI Requests Without Training Data and Knowing When to Train a Classifier\" | Hashnode"
description: "The point about labels being a snapshot is the one I'd stress for the later classifier stage. When you do start collecting labels, keep the capability catalog v"
url: "https://hashnode.com/posts/routing-ai-requests-without-training-data-and-knowing-when-to-train-a-classifier/6ad6ef8f2200e367f74f72af/comment/6ad72816579ec8a8ed2f43e3"
---

[Discussion](https://hashnode.com/posts/routing-ai-requests-without-training-data-and-knowing-when-to-train-a-classifier/6ad6ef8f2200e367f74f72af)

Comment [Routing AI Requests Without Training Data and Knowing When to Train a Classifier](https://hashnode.com/posts/routing-ai-requests-without-training-data-and-knowing-when-to-train-a-classifier/6ad6ef8f2200e367f74f72af)

[Yunus Emre Vurgun](https://hashnode.com/@yunusemrevurgun)

[Oct 20](https://hashnode.com/posts/routing-ai-requests-without-training-data-and-knowing-when-to-train-a-classifier/6ad6ef8f2200e367f74f72af/comment/6ad72816579ec8a8ed2f43e3)

The point about labels being a snapshot is the one I'd stress for the later classifier stage. When you do start collecting labels, keep the capability catalog version next to each one and hold out a test set per version, so a drop in accuracy after onboarding a new agent shows up as a routing regression and not as noise. Class imbalance will also bite early: a forecasting agent that gets 2% of traffic can look fine on overall accuracy while failing most of its requests, so report per-route recall alongside the abstain rate. A small hand-written set of the ambiguous multi-domain questions, like your sales-are-down example, makes a useful fixed regression suite.