Building a Production-Grade AI Pipeline: Scoring 10,000+ Listings Daily with LLMs

I learned the hard way that a working LLM pipeline and a production LLM pipeline are two different things.

When I first built the scoring system for a job board platform, I thought: throw GPT-4 at each listing, ask it to rate relevance, done. It worked for 100 listings. It worked for 1,000. Then I scaled to 10,000 listings a day and my OpenAI bill hit a number that made the client's eyes go wide.

That's when I stopped treating the LLM like a magic black box and started treating it like a production service with cost, latency, and failure modes.

Here's what actually matters when you're building an LLM pipeline at volume.

Function Calling Over Fine-Tuning