Testing AI: How to Effectively Evaluate LLMs

Traditional software testing rests on a basic assumption that given the same input, the system produces the same output. A test case defines expected behaviour, and a test passes or fails based on whether the output matches. This assumption – deterministic behaviour with verifiable correctness – is the foundation on which decades of quality assurance practices have been built. However, this can break down with large language models. An LLM may produce a different response to the same prompt on successive runs. Its outputs are sensitive to context, prompt phrasing, temperature settings and the interaction between retrieved documents and parametric knowledge. It can produce responses that are fluent, confident and completely wrong - a failure mode that traditional testing has no framework for detecting. And unlike a conventional software bug, which typically manifests consistently and can be reproduced, AI system failures are often probabilistic, context-dependent and difficult to predic