Confident AI

Confident AI – The DeepEval LLM Evaluation Platform

Confident AI offers the DeepEval LLM Evaluation Platform, a comprehensive solution to benchmark, safeguard, and improve LLM applications. It provides best-in-class metrics, guardrails, observability, and reproducible evaluation workflows to help teams iterate confidently at scale.

Key value proposition

• Benchmark and optimize LLM prompts, models, and configurations.
• Detect regressions and measure real-time production performance with robust metrics.
• Centralized tooling for dataset curation, evaluation, and monitoring.
• Open-source roots with strong industry adoption (daily evaluations, GitHub stars, and downloads).

Core components

• Dataset curation and annotation
• Run evaluations across multiple models/implementations
• Benchmarking with customizable metrics aligned to specific use cases
• Observability and monitoring of LLM outputs in production
• Safety, guardrails, and red-teaming support
• CI/CD-friendly pytest integration for unit testing LLM systems

How it works (overview)

• Curate datasets on Confident AI and pull from the cloud for evaluation.
• Run evaluations to compare different LLMs, prompts, and settings.
• Keep datasets up to date with realistic, production-grade data.
• Align metrics to your criteria and company values.
• Use observability tools to monitor and decide which real-world data to include in tests.

Note: The platform emphasizes open-source foundations and practical, production-focused evaluation.

Use cases

• Benchmarking new LLM models or prompt templates
• Detecting performance drift in production deployments
• Continuous evaluation in CI/CD pipelines
• Red-teaming and safety assessment of LLM outputs
• Data-driven tuning and cost optimization of LLM systems

How to get started

• Explore the platform with a free trial or request a demo
• Integrate Confident AI with your existing data pipelines and tooling
• Begin curating datasets and writing evaluation tests to measure your chosen metrics

Safety and ethics

• Focus on aligning metrics with company values and reducing risk in production deployments.
• Supports automated red-teaming and guardrails to identify potential safety concerns.

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How to Use Confident AI

1. Curate Datasets: Gather, annotate, and pull evaluation data from the cloud.
2. Run Evaluations: Benchmark LLMs and configurations using tailored metrics.
3. Monitor & Trace: Observe real-time outputs and decide which real-world data to include in tests.
4. Align Metrics: Customize metrics to your use case and values.
5. CI/CD Integration: Use Pytest integration to unit test LLM systems in your workflow.

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Core Features

• Centralized dataset curation and annotation
• Run evaluations across multiple LLMs and configurations
• Customizable evaluation metrics aligned to specific use cases
• LLM observability and real-time production performance insights
• Automated monitoring of LLM outputs for quality and safety
• Pytest integration for CI/CD-based testing
• Open-source foundations with active community and adoption
• Guardrails and red-teaming capabilities for safety assessment
• Stress-test and performance drift detection
• Production-ready workflows and scalable evaluation pipelines

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Why Confident AI

• 300,000+ daily evaluations
• 200+ GitHub stars
• 100,000+ monthly downloads
• Open-source and community-driven
• Designed to move fast without breaking things

Supporting evidence and ecosystem

• Documentation, blog posts, and tutorials to help teams adopt robust evaluation practices
• Example pipelines and test scripts to integrate into existing deployments
• Case studies illustrating cost savings and improved evaluation quality

Pricing & Availability

• Available as a product offering with a free trial and demos
• Open-source contributions encouraged through the referenced repositories

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Quick Start Resources

• Learn more on the official website and blog
• Explore deepeval and related tooling on GitHub
• Access tutorials and QuickStart guides to set up datasets, metrics, and tests