FareedKhan-dev/ai-agents-eval-techniques
Implementation of 12 AI agents evaluation techniques
This project offers practical methods for assessing the performance of AI-powered conversational agents and information retrieval systems (RAGs). It takes an AI system's responses or behaviors and applies various techniques to measure correctness, helpfulness, and efficiency, providing clear scores and insights into how well the AI is performing. AI developers, machine learning engineers, and product managers building or integrating AI agents would use this to ensure their systems meet quality standards.
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Use this if you are developing or deploying AI agents or RAG systems and need robust, practical ways to evaluate their output quality and decision-making processes.
Not ideal if you are looking for a simple pass/fail test for traditional software, as this focuses on the nuanced and often subjective evaluation of AI's generative or multi-step reasoning capabilities.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 31, 2025
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