.png)
.png)
.png)
Trustworthy AI: Automated Testing for LLM Applications
Gain insights into adversarial robustness, factual reliability and regulatory compliance. Build trust in your applications & ensure they stay within defined scope and regulations.
Keep your applications robust, reliable, and compliant. Identify unwanted behaviors and vulnerabilities.
.png)
Access varied adversarial, industry-specific, & compliance test benches. Customize as needed.
.png)
.png)
Scheduled or continuous quality assurance. Identify gaps & unwanted behavior. Guarantee strong performance.
.png)
Well-prepared overviews of evaluation results and error classification. Benfit from mitigation strategies.
Integrate effortlessly into any environment, no code changes needed. Continuously benchmark your LLM applications for confidence in release and operations.
Benefit from adversarial and use-case specific benchmarks to assess your applications as LLMs evolve further.
.png)
.png)
.png)
Uncover hidden intricacies in the behavior of LLM applications with a keen focus on addressing potential pitfalls. Navigating through these nuances is crucial, as failure to do so can lead to significant undesired behaviors and expose security risks.
Ensure corporate compliance and adherence to government regulations. Assess and document the behavior of your LLM applications to reduce the risk of non-compliance.
.png)
.png)
Ensuring consistent behavior is paramount for remaining reliable and robust. Erratic outputs in LLM applications, particularly in unusual or stressful conditions, can erode trust among users and stakeholders.
.svg)
Rhesis AI is instrumental in ensuring the robustness, reliability, and compliance of LLM applications. It achieves this by answering three fundamental questions essential for application assurance:
Are our applications robust to adverse behavior?
Rhesis AI assesses the robustness of LLM applications, identifying and mitigating potential adverse behaviors that could impact their functionality and performance.
Are our applications consistently exhibiting desired behavior?
Rhesis AI monitors the behavior of LLM applications to ensure consistency in performance and adherence to predefined standards and regulation.
Are our applications compliant with different regulations?
Rhesis AI evaluates the compliance of LLM applications with various regulations and standards, helping organizations meet legal and industry requirements.
LLM applications encompass numerous variables and sources of errors. Even when built upon seemingly safe foundational models, steering techniques like prompt-tuning and fine-tuning can introduce unexpected behaviors, raising significant concerns about their robustness, reliability, and compliance. Furthermore, essential elements such as retrieval augmented generation, meta prompts, system prompts, grounding, tone, and context all present potential sources of errors, emphasizing the critical need for ongoing assessment. Continuous evaluation is imperative for LLM applications.
The developers of leading foundational models regularly release new versions, showcasing improvements and changes. Consequently, models undergo continuous updates aimed at enhancing performance, which may have unclear impacts on LLM applications that depend on them. As these models evolve, testing becomes essential to ensure ongoing reliability, particularly in dynamic and ever-changing environments
Rhesis AI seamlessly integrates with existing architecture, requiring no code changes. It offers a systematic application assurance suite, including context and industry-specific test benches. Unlike manual benchmarking, which relies on ad-hoc prompts and subjective judgments, Rhesis AI provides consistent evaluations across different stakeholders. Enterprises benefit from comprehensive test coverage, particularly in complex and client-facing use cases.
