Rhesis AI Open-source test generation SDK for LLM applications

What is Rhesis AI?

Rhesis AI provides an open-source Software Development Kit (SDK) specifically engineered for generating robust test sets for Large Language Model (LLM) applications. It empowers development teams to create context-aware, multi-turn, and scenario-driven test cases precisely tailored to their application's unique requirements. The platform enhances AI evaluation processes by incorporating established industry standards from NIST, MITRE, and OWASP, facilitating thorough testing across critical dimensions including security, bias, reliability, and compliance.

The SDK promotes effective collaboration among developers, domain specialists, and compliance teams via human-in-the-loop evaluation mechanisms, enabling the iterative refinement of tests based on expert input. It seamlessly integrates into CI/CD pipelines, supporting automated and scalable testing procedures. Furthermore, Rhesis AI supplies pre-configured, domain-specific test benches suitable for sectors such as financial services and insurance. By automatically updating test suites with newly identified threats and adversarial patterns, Rhesis AI ensures that AI validation methods remain current and effective against evolving risks.

Features

Comprehensive Test Sets: Generate tests based on NIST, MITRE, and OWASP standards covering security, bias, reliability, and compliance.
Adaptive & Context-Aware Generation: Automatically create multi-turn, scenario-driven test cases tailored to specific applications, refining based on usage and feedback.
Domain-Specific Coverage: Utilize pre-built test benches for sectors like financial services and insurance.
Continuous Updates: Automatically integrate new adversarial patterns and risks to keep evaluations current.
Automated & Scalable Testing: Integrate with CI/CD pipelines for large-scale, repeatable AI validation.
Expert-Guided Collaboration: Enable human-in-the-loop evaluations, integrating feedback from developers, domain experts, and compliance teams.
Open-Source SDK: Provides a flexible and extensible foundation for test generation.
Integration with Evaluation Frameworks: Complements popular Gen AI test execution frameworks.

Use Cases

Testing LLM applications for security vulnerabilities.
Evaluating Gen AI models for bias and ensuring fairness.
Validating the reliability and consistency of AI application responses.
Ensuring AI systems comply with industry regulations (e.g., EU AI Act).
Automating the generation of large-scale test suites for AI validation.
Facilitating collaboration between technical and non-technical teams on AI testing.
Detecting sector-specific risks in AI applications for finance or insurance.
Integrating AI testing into CI/CD pipelines for continuous evaluation.

FAQs

Who is Rhesis AI designed for?

Rhesis AI is designed for professionals involved in developing, managing, or auditing Gen AI applications, including AI Engineers, Heads of AI Teams, AI Product Leads, AI Security Architects, Sr. AI Engineers, Data Scientists, Automation Engineers, Product Managers, Chief Technology Officers, and AI Solution Architects.
What industry standards does Rhesis AI utilize for testing?

The test generation is built on industry standards from NIST (National Institute of Standards and Technology), MITRE, and OWASP (Open Web Application Security Project).
Can Rhesis AI integrate with existing testing workflows?

Yes, the Rhesis SDK is designed to integrate into CI/CD pipelines and complements many popular Gen AI test execution frameworks.
How does Rhesis AI handle domain-specific testing requirements?

It offers pre-built, domain-specific test benches for sectors like financial services and insurance, and allows leveraging project assets and expert feedback to tailor tests.
Is Rhesis AI suitable for testing against new or emerging AI threats?

Yes, the SDK features automated test updates to continuously integrate new adversarial patterns and business-relevant risks, keeping evaluations current.