Testing an LLM-powered application requires more than checking whether an API returns HTTP 200. QA needs a systematic evaluation approach for correctness, relevance, consistency, safety and application-specific behavior.
Define What Good Looks Like
Before creating an evaluation suite, define the expected behavior of the application. Different use cases may prioritize factual correctness, relevance, completeness, tone, safety or adherence to business rules.
Create an Evaluation Dataset
Build a representative dataset containing normal requests, edge cases, ambiguous requests, adversarial inputs and known failure scenarios. The dataset becomes the foundation for repeatable AI regression testing.
Choose Evaluation Methods
Use a combination of deterministic assertions, semantic evaluation, model-based evaluation and human review. No single evaluator is sufficient for every AI application.
Track Evaluation Metrics
Useful metrics depend on the application but can include correctness, relevance, groundedness, response quality, refusal behavior, latency and cost.
Run AI Regression Testing
Every significant prompt, model, retrieval or application change should be evaluated against a regression dataset. This helps detect quality degradation that traditional functional tests may miss.
Senior QA Perspective
“I would build LLM evaluation around a controlled dataset and clearly defined quality dimensions. The evaluation pipeline should run automatically in CI/CD where practical and maintain historical results so the team can detect quality regressions across prompts, models and application versions.”
Interviewer Follow-ups
- • How would you create an LLM evaluation dataset?
- • When would you use an LLM-as-a-judge?
- • How would you measure hallucination?
- • How would you test RAG applications?
- • How would you compare two models?
- • How would you integrate AI evaluation into CI/CD?