I spent months building an AI scoring pipeline that processes over 10,000 job listings every day. The first version was a mess. Slow, expensive, and unreliable. The second version worked. Here's what I learned about the architecture decisions that actually matter when you put LLMs in a production data pipeline.
Most tutorials show you how to call an API and get a response. They don't show you what happens when you need to do that 10,000 times a day without burning your budget or losing accuracy. I had to figure that out the hard way.
The Problem With Naive AI Scoring
My first approach was embarrassingly simple. Take a job listing, dump the full text into a GPT prompt, and ask for a relevance score. It worked on three test listings. It fell apart at 500.
Three problems emerged immediately.
