# ai-quality-engineering

Published articles for ai-quality-engineering.

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## A Quality Engineering Framework for Testing AI, ML, and LLM Systems

DevFeed: [A Quality Engineering Framework for Testing AI, ML, and LLM Systems](<https://devfeed.tech/articles/a-quality-engineering-framework-for-testing-ai-ml-and-llm-systems-61495.md>)

Original publisher: [Read original article](<https://hackernoon.com/a-quality-engineering-framework-for-testing-ai-ml-and-llm-systems?source=rss>)

Author: Hemanth Srinivas Chittela

Published: 2026-09-28T16:02:02Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [Test automation](<https://devfeed.tech/topics/test-automation.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>)

Tags: [ai-driven-qa](<https://devfeed.tech/tags/ai-driven-qa.md>), [ai-in-qa-testing](<https://devfeed.tech/tags/ai-in-qa-testing.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [ai-quality-engineering](<https://devfeed.tech/tags/ai-quality-engineering.md>), [data-drift](<https://devfeed.tech/tags/data-drift.md>), [evals](<https://devfeed.tech/tags/evals.md>), [hackernoon-top-story](<https://devfeed.tech/tags/hackernoon-top-story.md>), [llm-optimization](<https://devfeed.tech/tags/llm-optimization.md>), [llm-test-time-compute](<https://devfeed.tech/tags/llm-test-time-compute.md>), [model-monitoring](<https://devfeed.tech/tags/model-monitoring.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [quality-engineering](<https://devfeed.tech/tags/quality-engineering.md>), [software-testing](<https://devfeed.tech/tags/software-testing.md>), [testing](<https://devfeed.tech/tags/testing.md>)

### AI overview

AI systems produce variable outputs and can drift as real-world data changes, so conventional pass/fail testing is inadequate. The article describes quality engineering approaches that combine data validation, statistical and semantic evaluation, model robustness checks, regression testing, and production monitoring.

### Source excerpt

AI systems need more than pass/fail tests. Here's how QE now spans data validation, model robustness, LLM evals, regression suites, and production monitoring.