In classic software testing we know the principle: defined input, expected output, clear result. For LLMs, however, the assessment is more complex. An answer may be semantically correct, but worded differently than expected. It may appear formally correct but contain a hallucination.
In addition, models change continuously through updates, prompt adjustments or fine-tuning. The central challenge is therefore: How can we measure the quality of a non-deterministic system in a reproducible and automated way?
This becomes particularly critical in productive applications such as the automated evaluation of customer feedback. If an LLM misclassifies the data, it can have a direct impact on support processes, escalations or management reports.
That was the reading sample of our heise Plus article “Testing Large Language Models with EVALs – Making Quality Measurable”. With a heise Plus subscription you can read the entire article.
