How do you test AI and LLM applications?
Testing AI and LLM applications is different from traditional testing because outputs are probabilistic, not fixed. Instead of exact-match assertions you evaluate behaviour: accuracy and faithfulness against a curated evaluation set, hallucination and bias rates, safety and toxicity, robustness to adversarial prompts, and retrieval quality for RAG systems. Evaluation runs continuously, because model and data changes shift behaviour over time.
Why can't you test LLMs like normal software?
Traditional tests assert that a given input produces an exact output. LLMs can return different valid answers to the same prompt, so exact-match assertions break. Testing shifts to evaluation: measuring whether outputs meet quality, safety, and faithfulness criteria across a representative set of cases, and tracking those metrics as the system changes.
This means building evaluation datasets, defining graded criteria (is the answer correct, grounded, safe, on-tone?), and often using a mix of automated scorers, model-based evaluation, and human review for the judgment calls.
What should an AI testing strategy cover?
Cover correctness and faithfulness (does the answer match ground truth and stay grounded in provided sources), hallucination and bias, safety and toxicity, and robustness to adversarial or malicious prompts such as prompt injection. For retrieval-augmented systems, test retrieval quality and permission-aware access separately from generation.
Wire these checks into the pipeline as evaluation gates so a model, prompt, or data change cannot ship if it regresses a key metric. Continuous evaluation matters because behaviour drifts as models and data evolve.
How Appsierra tests AI systems
Appsierra brings AI-native quality engineering: we build evaluation sets, automated and model-based scorers, adversarial and red-team tests, and pipeline evaluation gates — with senior engineers reviewing results so you can trust the numbers. Our work sits on an evaluation discipline we use in our own products, which is the accountable way to put AI in production.
See our generative AI development and AI governance & evaluation services to put a real testing-of-AI program in place.
Frequently asked questions
What is the difference between testing AI and traditional software testing?
Traditional testing checks fixed inputs against fixed outputs. AI testing evaluates probabilistic outputs against quality criteria — accuracy, faithfulness, safety, bias, robustness — across an evaluation set, and tracks those metrics continuously as the model and data change.
How do you test for hallucinations in an LLM?
You measure whether answers stay grounded in provided sources or known ground truth, using a curated evaluation set, automated and model-based faithfulness scorers, and human review for edge cases. The rate is tracked over time, not checked once.
What is adversarial or red-team testing for AI?
It deliberately probes an AI system with malicious or tricky inputs — prompt injection, jailbreaks, biased or unsafe prompts — to find failures before attackers or users do. It is essential for any AI feature exposed to real users.
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