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
5 DeepEval safety metrics
Evaluate models against Bias, Toxicity, Hallucination, Faithfulness, and Answer Relevancy with quantified scoring.
7 provider integrations
Test across OpenAI, Anthropic, Google, and 4 more providers with unified scoring for apples-to-apples comparison.
4-step evaluation wizard
Walk through Select model, Choose dataset, Configure judge LLM, Select metrics in a guided flow that makes comprehensive evaluations accessible to any team member.
Evaluation results dashboard
Compare metric scores across models with visual breakdowns of pass/fail rates per test prompt.
Built-in test datasets
Start evaluating with 11 curated prompts across Coding, Mathematics, Reasoning, Creative, and Knowledge categories.
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
Five core metrics powered by DeepEval: Answer Relevancy (does the response address the prompt?), Bias (does it show unfair preference?), Toxicity (is the content harmful?), Faithfulness (is it true to provided context?), and Hallucination (does it make things up?).
Seven providers: OpenAI, Anthropic, Gemini, xAI, and Mistral (cloud, API key required), plus Ollama and HuggingFace (local, no API key needed). You can evaluate models from any of these providers.
You configure a judge LLM (typically a capable model like GPT-4) that evaluates responses from your target model. The judge scores each response against the selected metrics, providing consistent automated evaluation at scale.
Yes. Beyond the 11 built-in prompts, you can provide custom datasets. The evaluation system accepts custom prompts and expected outputs for domain-specific testing.
More from AI tools
Related capabilities
Other features in the AI tools pillar
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See how VerifyWise can help you govern AI with confidence.