I once built an AI pipeline that was shut down after a single month. The LLM costs were unsustainable, and worse, the outputs were unreliable enough that we couldn't trust them in production. That failure taught me something I still use today: evaluation isn't a phase you add later. It's the scaffolding that makes the whole thing work.
If you're building an AI-powered MVP, you're probably torn between shipping fast and shipping something that doesn't embarrass you. You don't have time for a full observability stack or a human-in-the-loop review team. You need lightweight, automated checks that catch the worst failures without slowing you down. Here's what I've learned from shipping production LLM pipelines at scale.
Start With Structured Outputs, Not Free Text
The single highest-use thing you can do for reliability is to force the LLM into a structured schema. Free-text responses are a debugging nightmare. A JSON schema with typed fields and validation is something you can unit test.
On a production job board platform I built, every job listing goes through an LLM scoring pipeline that extracts structured data from raw ATS descriptions. The prompt uses function calling with a strict JSON schema. Here's a simplified version of the pattern:
