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title: README |
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--- |
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# Rhesis AI - Your LLM Application: Robust, Reliable & Compliant! |
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Rhesis AI provides an all-in-one AI testing platform for LLM (Large Language Models) applications. Their goal is to ensure the robustness, reliability, and compliance of LLM applications. Here are the key features: |
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1. **Quality Assurance at Scale**: Rhesis AI helps identify unwanted behaviors and vulnerabilities in LLM applications. It integrates effortlessly into any environment without requiring code changes. |
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2. **Benchmarking and Automation**: Organizations can continuously benchmark their LLM applications using adversarial and use-case specific benchmarks. This ensures confidence in release and operations. |
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3. **Uncover Hidden Intricacies**: Rhesis AI focuses on addressing potential pitfalls and uncovering hard-to-find 'unknown unknowns' in LLM application behavior. This is crucial to avoid undesired behaviors and security risks. |
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4. **Compliance and Trust**: Rhesis AI ensures compliance with regulatory standards and adherence to government regulations. It also enhances trust by ensuring consistent behavior in LLM applications. |
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## Frequently Asked Questions |
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1. **How does Rhesis AI contribute to LLM application assessment?** |
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- Rhesis AI assesses robustness, monitors behavior consistency, and evaluates compliance with regulations. |
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2. **Why is benchmarking essential for LLM applications?** |
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- LLM applications have numerous variables and potential sources of errors. Even when built upon safe foundational models, ongoing assessment is critical due to techniques like prompt-tuning and fine-tuning. |
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3. **Why is continuous testing necessary for LLM applications after deployment?** |
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- LLM applications evolve, and essential elements (retrieval augmented generation, meta prompts, etc.) introduce potential errors. Continuous evaluation ensures application reliability. |
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For more information, visit [Rhesis AI](https://www.rhesis.ai/about). |
