Evidently AI: AI Evaluation and LLM Observability

Key Features

• LLM Evaluation Metrics

  Includes over 100 built-in metrics to measure output accuracy, safety, and quality.

• Synthetic Data Generation

  Generates test inputs and adversarial scenarios to test AI resilience.

• LLM-as-a-Judge

  Uses external LLMs to automate the grading of AI responses based on specific criteria.

• ML Monitoring

  Tracks data drift and predictive quality for classifiers, recommenders, and regression models.

• Hallucination and PII Detection

  Identifies factually incorrect outputs and potential leaks of sensitive personal information.

• Open-Source Python Library

  Provides a library for running evaluations locally on a company's own infrastructure.

Use Cases

• RAG System Testing

  Evaluating retrieval quality and generation accuracy to help reduce hallucinations in chatbots.

• Adversarial Testing

  Testing AI agents against jailbreak attempts and harmful content prompts.

• Production Model Monitoring

  Tracking distribution shifts in production data to identify model drift over time.

• Multi-step Workflow Validation

  Testing AI agents that use tools and multi-step reasoning to validate outcomes.

FAQ

What can you evaluate with Evidently AI?

It supports generative AI tasks like RAG systems and AI agents, as well as predictive AI tasks including classification and recommendation systems.

Is there a free version of Evidently AI?

Yes, there is an open-source Python library and a free Developer plan for hobby projects and experiments.

How does the Pro plan differ from the Developer plan?

The Pro plan costs $80/month and increases limits to 100,000 rows per month, 100 GB of snapshots, and supports up to 5 seats.