Know how your LLMs perform before they reach production
Automated evaluation using LLM-as-a-Judge with DeepEval metrics for answer relevancy, bias, toxicity, faithfulness, and hallucination detection.
The challenge
You can't govern what you can't measure
Organizations deploy LLMs without systematic evaluation. Models go to production based on informal testing or vendor claims. When issues emerge—biased outputs, hallucinated facts, toxic responses—there's no baseline to compare against and no documentation of what was tested.
No standardized way to evaluate LLM quality before deployment
Manual testing is inconsistent and doesn't scale across models
Bias and toxicity issues discovered in production, not development
Different teams use different evaluation approaches with no comparability
Regulators ask for evidence of model testing and you have nothing to show
Benefits
Why use LLM evaluations?
Key advantages for your AI governance program
Evaluate any LLM from any provider in one interface
Use LLM-as-a-Judge for automated scoring
Detect bias, toxicity, and hallucinations before deployment
Compare models across standardized metrics
Capabilities
What you can do
Core functionality of LLM evaluations
DeepEval metrics
Five core metrics: Answer Relevancy, Bias, Toxicity, Faithfulness, and Hallucination—powered by LLM-as-a-Judge.
Multi-provider support
Evaluate models from OpenAI, Anthropic, Gemini, xAI, Mistral, Ollama, and HuggingFace in a unified interface.
Built-in test datasets
11 curated prompts across 5 categories: Coding, Mathematics, Reasoning, Creative, and Knowledge.
Guided evaluation wizard
4-step process: Select model → Choose dataset → Configure judge LLM → Select metrics. No code required.
How it works
See it in action
Explore the key functionality of LLM evaluations
Evaluation dashboard
Monitor LLM performance across safety, accuracy, and bias metrics
Evaluation results
Drill into detailed test results with examples and recommendations
Enterprise example
How an organization established LLM quality gates
See how organizations use this capability in practice
The challenge
An organization was deploying multiple LLMs across different business units. Each team had their own informal testing process—some used manual spot checks, others ran a few prompts and called it done. When a customer-facing chatbot started providing biased responses, leadership realized they had no systematic evaluation process.
The solution
They implemented a standardized evaluation workflow. Every LLM must pass through the evaluation system before deployment, measuring Answer Relevancy, Bias, Toxicity, Faithfulness, and Hallucination. Results are stored with each model version in the model inventory.
The outcome
The organization now has quality gates for LLM deployment. Models that score below thresholds on Bias or Toxicity don't reach production. When new model versions are released, they're evaluated against the same metrics for direct comparison. Audit documentation shows systematic testing for every deployed model.
Why VerifyWise
Systematic evaluation for every LLM
What makes our approach different
LLM-as-a-Judge architecture
Use a judge LLM (like GPT-4) to automatically score your target model's responses. Consistent, scalable evaluation without manual review.
Five standardized metrics
Answer Relevancy, Bias, Toxicity, Faithfulness, and Hallucination. Each metric provides a clear score you can track over time and across model versions.
Any model, any provider
Evaluate OpenAI, Anthropic, Gemini, Mistral, or local models via Ollama and HuggingFace. One interface for your entire model portfolio.
No-code and code options
Use the 4-step wizard in the UI for quick evaluations, or the Python CLI with YAML configs for automated pipelines.
Regulatory context
Evaluation supports AI safety requirements
AI regulations require organizations to test and validate AI systems before deployment. Systematic LLM evaluation provides evidence that models have been assessed for quality and safety.
Article 9 requires testing and validation as part of the risk management system. Article 15 requires accuracy, robustness, and cybersecurity testing for high-risk AI.
Clause 8.4 requires verification and validation of AI systems. Documented evaluation results demonstrate this requirement is met.
Industry frameworks for responsible AI emphasize pre-deployment testing for bias, fairness, and safety. Evaluation metrics provide quantifiable evidence.
Technical details
How it works
Implementation details and technical capabilities
DeepEval integration for standardized LLM evaluation metrics
5 core metrics: Answer Relevancy, Bias, Toxicity, Faithfulness, Hallucination
7 model providers: OpenAI, Anthropic, Gemini, xAI, Mistral, Ollama (local), HuggingFace (local)
11 built-in test prompts across Coding (3), Mathematics (2), Reasoning (2), Creative (2), Knowledge (2)
LLM-as-a-Judge architecture: Configurable judge model scores target model responses
YAML-based configuration for custom evaluation setups
Frontend wizard and Python CLI for flexible evaluation workflows
Supported frameworks
Integrations
FAQ
Common questions
Frequently asked questions about LLM evaluations
More from AI tools
Related capabilities
Other features in the AI tools pillar
Ready to get started?
See how VerifyWise can help you govern AI with confidence.