Test your GenAI systems before they reach users

GenAI testing

Test your GenAI systems before they reach users

Openlayer gives teams the tools to evaluate generative AI systems across edge cases, hallucinations, prompt quality, and more.

Why GenAI testing is different

Generative AI fails differently than traditional ML

LLMs and other generative systems are flexible—but unpredictable. Unlike ML models that output structured values, GenAI outputs are open-ended, which makes them harder to evaluate and test.

What GenAI testing should cover

From hallucination to helpfulness

Output hallucination checksVerify that the model’s responses stay grounded in source facts rather than inventing new ones.

Relevance and factuality evaluationScore each answer for on-topic coverage and objective correctness so users get useful, accurate information.

Toxicity and safety scoringFlag hateful, harassing, or self-harm content before it ever reaches production.

Prompt injection defense testingProbe for jailbreaks and malicious instructions to make sure guardrails hold under attack.

Rubric-based or LLM-as-a-judge scoringApply structured grading guidelines—or enlist a reference model—to evaluate nuance at scale.

Regression testing across prompt changesCompare new prompt versions against baselines to catch unseen performance drops and unintended behavior shifts.

Openlayer's approach

Test GenAI apps the way they'll actually be used

Run evaluation tests on real-world prompts and flows

Track system performance across models and versions

Use human, automated, or LLM-based scoring

Analyze system behavior in depth—before production

Supports chatbots, copilots, search assistants, and more

FAQs

Your questions, answered

What is GenAI testing?

What's the best way to test GenAI?

Does Openlayer support prompt or system-level testing?

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GenAI testing