# Evidently AI - AI Evaluation & LLM Observability Platform

## About

Ensure your AI is production-ready. Test LLMs and monitor performance across AI applications, RAG systems, and multi-agent workflows. Built on open-source.

- Verified: Yes

## Services

### AI Testing and Monitoring
- [AI Evaluation & Observability](https://bilarna.com/ai/ai-testing-and-monitoring/ai-evaluation-and-observability)

### AI Security & Compliance
- [AI Safety & Risk Management](https://bilarna.com/ai/ai-security-and-compliance/ai-safety-and-risk-management)

## Pricing

- Model: subscription

## Frequently Asked Questions

**Q: Why is testing AI models important before deployment?**
A: Testing AI models before deployment is crucial because AI systems, especially large language models (LLMs), behave differently from traditional software. They can produce unpredictable outputs, confidently generate incorrect information, and may inadvertently expose sensitive data. Testing helps identify these issues early, ensuring the AI performs reliably in real-world scenarios. It also helps detect vulnerabilities such as susceptibility to malicious prompts or unexpected inputs that could degrade performance or cause failures. Proper evaluation and monitoring ensure that AI applications remain safe, accurate, and trustworthy when integrated into production environments.

**Q: What are common risks associated with large language models in AI applications?**
A: Large language models (LLMs) in AI applications pose several risks due to their non-deterministic nature. They may confidently generate fabricated or misleading information, which can harm user trust. Unexpected or adversarial inputs can cause the model to produce low-quality or inappropriate responses. Sensitive or confidential data might inadvertently be included in outputs, leading to privacy concerns. Additionally, malicious actors can exploit prompt injection attacks to manipulate the AI’s behavior, potentially causing security breaches or spreading harmful content. Understanding and mitigating these risks through thorough evaluation and monitoring is essential to maintain safe and effective AI deployments.

**Q: How can AI performance be effectively monitored in production environments?**
A: Effective monitoring of AI performance in production involves continuous evaluation of model outputs and behavior to detect anomalies, degradation, or unexpected results. This includes tracking metrics such as accuracy, response quality, and latency across various AI applications and workflows. Monitoring should also focus on identifying sensitive data leakage, inappropriate content, and potential security threats like prompt injections. Utilizing open-source tools and platforms designed for AI observability can facilitate real-time insights and alerts. Regular testing with diverse inputs, including adversarial cases, helps maintain robustness. Overall, a comprehensive observability strategy ensures AI systems remain reliable, safe, and aligned with business objectives after deployment.

## Links

- Profile: https://bilarna.com/provider/evidentlyai
- Structured data: https://bilarna.com/provider/evidentlyai/agent.json
- API schema: https://bilarna.com/provider/evidentlyai/openapi.yaml