Deep dive
LLM Testing for QA Engineers
An LLM feature fails differently from a login form. It rarely throws an error — it answers confidently and wrongly. That makes the tester's job about evidence: what can you assert, repeatedly, about a system that is allowed to vary?
The roadmap, stage by stage
What you can actually assert
Move from exact-match assertions to property-based ones: structure, grounding, safety, refusal behaviour and consistency across repeated runs.
- Schema / structured-output validation
- Grounding against provided context
- Refusal and safety behaviour
- Run-to-run consistency
Prompt regression suites
Treat every prompt change like a code change: a fixed dataset of inputs, expected properties and a diff of behaviour before and after.
Hallucination and grounding
For retrieval-backed answers, verify each claim traces to retrieved context. Ungrounded confidence is the defect class that matters most.
Non-functional: latency, cost, limits
LLM features have a bill and a latency budget. Load-test them like any other backend, and track token cost per journey.
Skills you'll gain
- Prompt regression testing
- Grounding and hallucination checks
- Structured-output validation
- Safety and refusal testing
- LLM latency and cost testing
Why it matters for your career
LLM testing skills sit at the top of most AI-product QA job descriptions, and they transfer across every team shipping an assistant, copilot or RAG search feature.
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FAQ
Can I automate LLM tests in CI?
Yes. Fix the temperature, use a stable evaluation dataset, and assert on properties rather than exact text.
How do I test for hallucinations?
Require every factual claim to be traceable to the retrieved context, and score answers that are not.
Do I need to load-test an LLM feature?
If users wait on it, yes — latency and cost per request are core quality attributes.
Not sure where you fit on this roadmap?
Take the quiz for a personalised path, or ask Tia — she'll build a plan around your experience.