LLM evals: build a funnel, not a fork

Spotify’s engineering blog dropped a note on how to run better experiments with large-language-model evaluations. The short version: treat evals like a funnel, not a fork. Don’t scatter test cases everywhere. Start small, add layers, and measure what changes.

The post walks through why teams get stuck when they try to evaluate models too broadly too soon. Instead of splitting work into a dozen random test buckets, you build a sequence — basic checks, then harder ones, then edge cases. Each layer filters out noise before the next one adds complexity.

The idea is practical. It’s not a new framework. It’s a way of organizing test data so you can actually see what breaks, when it breaks, and what the model is doing wrong at each step. If your evals are a fork, you’re guessing. If they’re a funnel, you’re measuring.

The post links to Spotify’s own engineering page for more detail. TLDR Data summarized it, and the source blog by Brooker also ties into a broader post on what’s easy versus what’s hard with LLMs.

Why this matters for us:
If you’re running AI tools in your shop or building something for la gente, stop treating evals like a to-do list and start treating them like a funnel — because when the model fails, you need to know exactly where, not just that it failed.