The first time I ran an LLM scoring pipeline against a large batch of job listings, the results looked great on paper. Every listing had a score. Every score had a confidence level. The numbers were well distributed. I felt good about it for a while.
Then I spot-checked some random outputs. Most of them were wrong. The LLM had given high scores to irrelevant listings, low scores to perfect matches, and fabricated entire categories of data. The confidence levels meant nothing. The system was confidently wrong at scale.
That's the eval trap. You build a pipeline, it runs, it produces numbers, and you think you're done. You're not. Evaluating AI outputs is itself an engineering challenge, and if you treat it as an afterthought, your AI feature will ship broken.
I've been building production AI systems for a while now, including a high-traffic job board that processes over 10,000 listings daily with an LLM scoring pipeline. Here's what I've learned about making evals that actually tell you the truth.
Structured Output Is Your First Line of Defense
