Rigorous evaluation for GenAI and LLMs

LLM evaluation

Rigorous evaluation for GenAI and LLMs

Test every prompt, trace every response, and validate every output with Openlayer.

Core features

Explore the platform

Evaluate your AI systems with tests

Use 100+ built-in and customizable tests—including LLM-as-a-judge—to evaluate hallucination, completeness, relevance, and more.

Track and test every change

Compare performance across prompts, model providers, or any change to your system. Avoid regressions and spot improvements.

Use your favorite tools and frameworks

Seamlessly integrate with any LLM provider, Git-based workflows, and your existing stack via SDKs and APIs.

Why it matters

LLMs are powerful, but unpredictable

Hallucinations, inconsistent outputs, and unclear evaluation metrics make deploying LLMs risky. Without structured testing, teams are left guessing what works. Openlayer brings rigor to LLM evaluation so you can benchmark, iterate, and ship with confidence.

Use cases

Purpose-built for complex GenAI apps

Whether you're building AI copilots, summarization tools, or customer support agents, Openlayer helps you test performance across prompts, data types, and model settings—including hallucination, toxicity, relevance, and fluency.

Why Openlayer

Standardize evaluation across GenAI systems

Use objective and subjective scoring methodsBlend hard metrics (accuracy, latency, cost) with human-in-the-loop judgments to capture nuance and quality.

Validate outputs across edge casesStress-test models on adversarial, long-tail, and safety-critical scenarios before users ever see them.

Collaborate with product and research teamsGive PMs, UX, and researchers a shared workspace to review results, leave feedback, and iterate faster.

Scale evaluation with automation, not headcountRun thousands of tests on every pull request—no need to hire armies of manual reviewers.

Integrations

Plug into your GenAI stack

Supports OpenAI, Anthropic, Hugging Face, LangChain, and more. Trigger tests via CLI or GitHub Actions. Integrates with prompt orchestration layers and analytics tools.

Customers

Confidence before launch

“We rolled out our chatbot with zero hallucinations in testing—and we couldn’t have done it without Openlayer.”

Sr. Software Engineer at Fortune 500 Financial Institution

FAQs

Your questions, answered

How do you evaluate an LLM model?

Openlayer offers both quantitative and qualitative evaluation techniques, including model-graded outputs (LLM-as-a-judge), rubric scoring, and prompt test coverage.

What kinds of models are supported?

We support all LLMs—OpenAI, Anthropic, open source models, and even custom fine-tuned ones.

Can I test non-text outputs like JSON or code blocks?

Yes, Openlayer is built to handle structured outputs, tool calls, and more.

Do you support prompt versioning and rollback?

Absolutely. Every version and result is tracked so you can compare and revert with ease.

$ openlayer push

LLM evaluation