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How to Evaluate LLM Outputs in Production: A Practical Guide
Most LLM applications are deployed without a meaningful evaluation system. The developer prompts the model a few times, the outputs look reasonable, and it ships. Then users start complaining about specific failure cases, the developer adjusts the prompt, checks a few examples again, and ships again. This cycle is not engineering. It is guessing. Evals are what turns LLM development from guessing into engineering. They let you measure whether a change actually improved things, catch regressions when you update your prompt or switch models, and understand the failure modes of your application before users do. This guide covers how to build an eval system that is actually useful, not just theoretically correct. ...