Confident AI

89%

COVERAGE

RAG Eval Metrics

Context precision, recall, faithfulness, answer relevance — foundational metrics for RAG quality.

Hallucination Detection

Detect fabricated, unsupported, or factually incorrect content. Compare outputs against source documents.

Custom / Domain Metrics

Define custom evaluation criteria using G-Eval, code-based scorers, or domain-specific rubrics.

LLM-as-Judge

Use LLMs to evaluate LLM outputs programmatically. Configure judge models, criteria, and scoring rubrics.

Red Teaming / Adversarial

Automated adversarial testing — prompt injection, jailbreaks, toxicity probes, bias elicitation.

Safety & Bias Testing

Test for toxic outputs, harmful content, demographic bias, stereotyping, and safety policy violations.

Agent / Multi-step Eval

Evaluate multi-step agent workflows — tool call accuracy, decision path quality, goal completion.

CI/CD Integration

Run evals as automated tests in CI/CD pipelines. Fail builds on quality regression.

Dataset Management

Create, version, curate eval datasets. Synthetic data generation. Golden dataset from prod traces.

Human-in-the-Loop

Annotation queues, human review workflows, inter-annotator agreement, SME feedback collection.

Experiment Tracking

Track and compare eval runs across prompt versions, model changes, and config updates.

Production Monitoring

Continuous evaluation of live production traffic — real-time quality scoring, alerting on degradation.

Tracing & Observability

Distributed tracing of LLM calls, retrieval steps, tool invocations. Debug failing evaluations.

Open Source / Self-hosted

Open-source availability with self-hosting. Data residency, air-gapped, no vendor dependency.