Test case management, automated evaluation runs, scheduled testing, feedback collection, and pass/fail criteria
AI quality is not something you check once and forget. OpenRails' Evaluation & Testing framework provides continuous quality assurance for AI responses, agent workflows, and RAG retrieval accuracy. Define test cases with expected outcomes, run them automatically on a schedule, and track quality metrics over time. When models change, documents are updated, or prompts are modified, the evaluation framework catches regressions before they reach users.
Key Value: Most AI platforms lack built-in quality assurance. OpenRails treats AI testing as a first-class feature, giving teams confidence that their AI produces accurate, consistent results — and alerting them when it does not.
Create, organize, and version test cases. Each test case defines an input (question or task), expected output criteria, and evaluation method. Group test cases by category, feature, or criticality level.
Execute test suites against your AI configuration. Runs test each case against the current model, prompts, and knowledge base. Results include pass/fail status, response quality scores, latency, and token usage.
Configure test suites to run automatically on a schedule — daily, weekly, or after specific events (model update, document re-ingestion). Receive alerts when quality drops below thresholds.
Collect user feedback on AI responses (thumbs up/down, comments, corrections). Feedback data feeds into evaluation metrics and can be used to generate new test cases from real-world interactions.
Each evaluation run measures responses across multiple dimensions. Configure pass thresholds to automatically flag regressions and quality issues.
Track quality metrics over time with visual dashboards. See pass rates, regression trends, response latency distributions, and comparison across model versions or prompt iterations.
Each evaluation run captures multiple dimensions of response quality:
How well does the response answer the question? Compared against expected outputs for each test case.
How confident is the model in its response? Helps identify cases where the AI is uncertain or guessing.
How long does it take to generate a response? Track performance across models, knowledge bases, and query complexity.
The full response text is captured and stored for human review, comparison across runs, and trend analysis.
Beyond quality metrics, OpenRails evaluations can validate that your security boundaries are working correctly. Run evaluations at a specific user or permission level to verify that restricted content never leaks into AI responses.
Run an evaluation as a specific user to confirm the AI only returns content that user is authorized to see. Catch misconfigured permissions before they become a data breach.
Test at a specific security tier to verify that higher-tier documents are never included in responses. Prove to auditors that your access controls are enforced end-to-end — including through the AI layer.
Why this matters: Most AI platforms test for response quality but not for data leakage. OpenRails lets you prove that your security boundaries hold even when content passes through LLMs — a critical requirement for regulated industries.
Define Test Cases → Configure Evaluation Criteria → Run Test Suite → Review Results → Iterate on Prompts/Config → Schedule for Continuous Monitoring
Feedback loop: User feedback generates new test cases | Model changes trigger automated regression runs
Validate that switching LLM providers or model versions maintains response quality
A/B test prompt variations with quantified quality metrics to find optimal configurations
Ensure AI responses meet regulatory requirements with automated compliance test suites