A Quality Engineering Framework for Testing AI, ML, and LLM Systems
For two decades, quality engineering (QE) rested on a comfortable assumption: given the same input, a system produces the same output, and a test either passes or fails. Data pipelines, machine learning (ML) models, and large language models (LLMs) break that assumption at a fundamental level. As AI moves from experimental notebooks into production systems that make decisions, generate content, and act autonomously, quality engineers are being asked to validate systems that are inherently non-deterministic, statistically defined, and constantly drifting. This shift is not an extension of traditional QA — it is a distinct discipline that borrows from data engineering, statistics, and ML operations while keeping the QE mandate of building trust before release.
Why Traditional QA Breaks Down for AI Systems
Traditional software testing relies on deterministic assertions: a given input maps to one correct output, and deviation is a bug. AI systems invert this logic. The same prompt...
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