Quick answer: Build eval suites for LLM apps — accuracy, hallucination rate, regressions.
LLM Evaluation is the practice of systematically measuring how well large language models perform on your specific tasks. It involves building test suites that measure accuracy, detect hallucinations (false or made-up information), catch performance regressions, and quantify quality metrics like latency and cost-per-request. Rather than shipping an LLM application and hoping it works, evaluation lets you benchmark different model versions, compare approaches, and catch breaking changes before production. For example, you might build an eval suite that tests whether your customer support chatbot gives factually correct answers 95% of the time, or whether your code generation tool produces compilable Python functions.