I've watched teams spend weeks refining an LLM scoring pipeline, only to run it against real data and discover that many of the scores are useless. The model rewards keyword density over actual relevance. The output looks structured. The numbers are in range. But the results don't match what a human would judge.
That's the moment you realize: you don't ship AI features by writing prompts first. You ship them by writing the evaluation first.
The 80/20 Trap Nobody Talks About
Most teams building AI features follow the same pattern. They wire up an LLM call, test it on three examples, and call it done. Then production hits and they discover the model hallucinates on edge cases, ignores instructions, or produces output that looks right but is wrong.
The problem isn't the model. It's that you evaluated your system on the wrong thing.
