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---
title: README
emoji: 🦀
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colorTo: green
sdk: static
pinned: false
---
# Rhesis AI - Your LLM Application: Robust, Reliable & Compliant!
<img src="rhesis_logo.png" alt="Rhesis Logo" width="30%" />
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:
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.
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.
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.
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.
## Frequently Asked Questions
1. **How does Rhesis AI contribute to LLM application assessment?**
- Rhesis AI assesses robustness, monitors behavior consistency, and evaluates compliance with regulations.
2. **Why is benchmarking essential for LLM applications?**
- 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.
3. **Why is continuous testing necessary for LLM applications after deployment?**
- LLM applications evolve, and essential elements (retrieval augmented generation, meta prompts, etc.) introduce potential errors. Continuous evaluation ensures application reliability.
For more information, visit [Rhesis AI](https://www.rhesis.ai/about). |