ecnusse/Hydrangea
📦 Open Source Projectecnusse
A comprehensive defect library designed to identify and categorize failures in LLM-enabled software systems.
Hydrangea serves as a specialized knowledge base and diagnostic tool for engineers building LLM-integrated software. As AI applications move from prototypes to production, they face unique challenges such as hallucinations, context window limitations, and retrieval inaccuracies in RAG pipelines. Hydrangea addresses these by providing a systematic defect library that allows developers to categorize these issues effectively.
The project focuses on the intersection of traditional software engineering and modern AI development. It provides the necessary structure to track bugs that are specific to non-deterministic LLM outputs, enabling teams to build more robust testing suites and debugging workflows. By utilizing this library, developers can better understand the failure patterns of their agents and implement targeted mitigation strategies, ultimately leading to more stable and trustworthy AI deployments.
💡Highlights
- ├─Standardized LLM defect taxonomy
- ├─Tailored for RAG-based systems
- └─Systematic failure analysis
🎯For
- ├─AI Engineers
- └─QA Engineers