Dynatrace expands AI Observability with LLM quality metrics
Dynatrace is adding new ways to check the quality of large language models (LLMs), not just their speed and uptime. Experts say LLMs may give answers that sound good but can be wrong or biased, so teams now watch extra metrics like accuracy and fairness. The latest advice suggests tracking things like error rates, cost, and answer quality, and connecting these with normal performance data. Quality checks may include human review for risky tasks and scheduled tests to spot problems early. Reports suggest that trust in AI systems may rise when companies combine good monitoring, clear rules, and responsible behavior from leaders.
