Master Patronus AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for a free Patronus AI account at patronus.ai and complete the onboarding process: 5
10 minutes Upload or create evaluation datasets relevant to your AI application and quality criteria: 15
30 minutes Configure evaluators and guardrails, then integrate with your application via API or SDK: 30
💡 Quick Start: Follow these 3 steps in order to get up and running with Patronus AI quickly.
Explore the key features that make Patronus AI powerful for ai evaluation workflows.
Score LLM outputs across quality dimensions including accuracy, relevance, coherence, and safety using pre-built and custom evaluators.
Running nightly evaluations against a test dataset to track RAG application accuracy and detect quality regressions.
Specialized models identify when LLM responses contain information not supported by provided context or known facts, with claim-level granularity.
Detecting when a customer support bot claims a product has features it doesn't actually have.
Input/output filtering for PII detection, content safety, prompt injection prevention, and custom policy enforcement.
Blocking responses that contain customer phone numbers or credit card information before they're displayed.
Adversarial testing workflows that help discover AI application vulnerabilities and failure modes.
Discovering that a chatbot can be manipulated into bypassing content policies through specific prompt patterns.
Define domain-specific evaluation criteria using natural language descriptions or code-based scoring functions.
Creating an evaluator that checks whether medical AI responses include appropriate disclaimers and safety warnings.
Run evaluations as part of development pipelines to catch quality issues before deployment, with pass/fail gates based on score thresholds.
Failing a deployment pipeline when hallucination rates exceed 5% on the evaluation test set.
Patronus AI is best used for evaluating and governing production LLM, RAG, and agent systems. It is especially relevant when teams need hallucination detection, explainable LLM judges, red-teaming, guardrails, and observability in a single workflow. Based on our analysis of 870+ AI tools, Patronus is a stronger fit for enterprise AI safety and quality programs than for simple one-off prompt experiments.
The current tool data identifies Lynx as Patronus AI's hallucination-detection model. Lynx is designed to evaluate whether model outputs are supported by the provided context, which is particularly important for RAG systems. Accuracy will still depend on the quality of the source context, the evaluation dataset, and the thresholds a team configures for its use case.
Yes. Patronus supports custom evaluators for domain-specific checks, including natural-language criteria and code-based scoring functions according to the existing product data. This is useful for teams that need to evaluate legal compliance, medical safety language, brand voice, internal policy adherence, or other rules that generic evaluators will not understand reliably.
Yes. The current data states that Patronus provides CLI tools and API endpoints for running evaluations in CI/CD pipelines. Teams can configure pass/fail gates, such as blocking a deployment when hallucination rates exceed a defined threshold like 5% on a test set. This makes it useful for catching prompt, model, or retrieval regressions before they reach production users.
Patronus AI has a free Developer tier with up to 2 projects, 5 experiments per project, 2-week retention, unlimited comparisons and dataset access, and $10 in API credits. Paid API usage is listed at $10 per 1,000 small evaluator calls, $20 per 1,000 large evaluator calls, and $10 per 1,000 evaluation explanations. Enterprise pricing remains custom and requires contacting sales.
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Tutorial updated March 2026