Evidently AI

What is Evidently AI?

Evidently AI is an AI evaluation and LLM observability platform built for testing and monitoring AI systems after changes are deployed. Its core purpose is to help teams verify that models behave safely and reliably in production-like conditions—so they can detect failures such as hallucinations, unsafe outputs, and regressions across updates.

The platform is built on top of Evidently, an open-source AI evaluation tool, and includes “100+ metrics” that can be extended. Evidently AI supports evaluation for AI applications including RAG pipelines and multi-step workflows, with continuous testing driven by a live dashboard.

Key Features

• Automated LLM evaluation with shareable reports: Measures output accuracy, safety, and quality and reports where AI breaks “down to each response.”
• Synthetic data for realistic and adversarial inputs: Generates edge-case and hostile test prompts tailored to a given use case, including examples ranging from harmless prompts to attacks.
• Continuous testing and live observability dashboard: Tracks performance across every update to help catch drift, regressions, and emerging risks earlier.
• Evaluation coverage for common failure modes: Includes capabilities for hallucinations and factuality, PII detection, and other quality signals such as adherence to guidelines/format and retrieval-related issues.
• Custom evaluation definitions and metric library: Uses a library of 100+ in-built metrics, and supports adding custom metrics with combinations of rules, classifiers, and LLM-based evaluations.

How to Use Evidently AI

1. Start from existing metrics and evaluations: Use the platform’s built-in evaluation components (including the 100+ in-built metrics) to define what “good” looks like for your AI.
2. Generate test inputs: Create synthetic data that reflects typical requests plus edge cases and adversarial prompts relevant to your system.
3. Run automated evaluations and review results: Execute evaluations to produce a clear report identifying failures at the response level.
4. Enable continuous monitoring: Track evaluation results across updates using the live dashboard to spot drift and regressions.

Use Cases

• Adversarial testing for safety: Probe an AI system for risks such as PII leaks, jailbreaks, and harmful content before those issues reach users.
• RAG evaluation for retrieval quality: Test retrieval accuracy in RAG pipelines and chatbots to help reduce hallucinations and assess context relevance.
• Evaluation for multi-agent or agentic workflows: Validate multi-step workflows, reasoning, and tool use by checking system behavior beyond single responses.
• Monitoring predictive systems and ML components: Continuously evaluate classifiers, summarizers, recommenders, and traditional ML models using the same evaluation/monitoring approach.
• Custom quality systems for domain-specific rules: Combine rules, classifiers, and LLM-based evaluations to measure adherence to guidelines and formats that are specific to your application.

FAQ

• What does Evidently AI evaluate? It evaluates AI outputs for accuracy, safety, and quality, including signals such as hallucinations/factuality, PII detection, and retrieval quality for RAG systems.

• How does continuous testing work? The platform tracks performance across updates using a live dashboard, aimed at helping teams catch drift, regressions, and emerging risks.

• Do I need to build evaluations from scratch? No. The platform provides 100+ in-built metrics and supports creating custom evals, including combinations of rules, classifiers, and LLM-based evaluations.

• Does Evidently AI support adversarial testing? Yes. It provides synthetic data generation for realistic edge cases and adversarial inputs, including hostile attacks.

• Is Evidently AI related to Evidently open source? Yes. Evidently AI is built on top of Evidently, described as a leading open-source AI evaluation tool.

Alternatives

• Open-source LLM evaluation frameworks: These can provide evaluation logic and metrics but may require more effort to build full observability/continuous monitoring workflows.
• General-purpose monitoring/observability platforms for ML: Useful for production monitoring, but may not natively include LLM-focused evaluation patterns like response-level failure analysis and LLM-as-judge workflows.
• RAG-specific evaluation tooling: Focuses on retrieval and generation quality; these alternatives can be narrower than Evidently AI’s broader approach across safety, quality metrics, and continuous testing.
• Model evaluation tooling embedded in CI pipelines: Helps run tests on each change, but may lack the same breadth of metric coverage and an integrated live dashboard for ongoing observability.