AI systems do not behave like traditional software. The same input can produce different outputs. That makes testing more challenging, but also more important. Systematic testing is the only way to have confidence in the quality of your AI system.
In traditional software you test whether the output equals what you expect. In AI systems, the output is rarely identical from run to run. Yet you want to know whether the system performs consistently well. That requires a different testing method: evaluation-based testing rather than deterministic comparison.
A traditional software test compares output to an expected value: is the output equal to X? AI output is rarely exactly the same twice. "What are the opening hours?" can be answered with "We are open from 9 to 5" but also "Our opening hours are 09:00 to 17:00." Both are correct; they are not identical.
AI testing is therefore about quality assessment, not equality comparison. Is the answer correct? Is it complete? Does it match the tone? Are there harmful or incorrect elements?
The foundation of systematic AI testing is a test set: a collection of input questions or prompts with corresponding criteria for a good answer. Those criteria do not need to be exact strings; they can be evaluation dimensions.
Example for a customer service chatbot:
Build a test set of at least 50-100 cases. Ensure those cases cover the variety you expect in production: different phrasings, edge cases and questions outside the domain.
For small test sets, manual evaluation is feasible and provides the most reliable results. A human rates each answer on the relevant dimensions. That is time-consuming but delivers the highest quality feedback.
For larger test sets or frequent evaluation runs, you can use LLM-as-judge: you have another AI model rate the output based on an evaluation rubric. That scales better, but introduces the errors of the evaluation model. Use this as a supplement to manual evaluation, not a replacement.
Relevant evaluation dimensions for most AI applications:
Define a rating scale for each dimension: correct/incorrect, or a 1-5 scale for qualitative dimensions.
Regression testing means that after every change to the system, a new system prompt, updated knowledge base, different model, you run your full test set again. That way you know whether the change improved or worsened quality.
This sounds simple but is often skipped. Without regression testing you do not know whether an "improvement" has created problems elsewhere.
If you have two versions of a system prompt, or want to compare two model choices, you use A/B testing. Give both versions the same test set and compare the scores. The version with consistently better scores wins.
Ensure the test set is large enough for statistically meaningful conclusions. With small test sets, score differences are often noise, not signal.
After going live, you monitor quality through production monitoring. Analyse conversations with low ratings, questions that lead to fallbacks and escalations. Add those cases to your test set so the system continuously improves on real user questions.
Systematic AI testing requires a different mindset than traditional software testing, but the basic principles are comparable: define what good looks like, test it, measure it, improve it. Mach8 helps organisations set up testing frameworks that keep AI systems reliable and high-quality.
Want to set up a solid testing method for your AI application? Get in touch with Mach8.
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