Evaluating RAG Model Performance Metrics, Bias, and Interpretability | Amir Siddiqui | DSC MENA 25

Description

Evaluating Retrieval-Augmented Generation (RAG) models is essential for ensuring the reliability of AI-driven search and knowledge retrieval systems. In this session, Amir Siddiqui explored the key challenges of assessing RAG performance, focusing on important aspects such as: - Performance Metrics – Amir discussed how to assess retrieval accuracy, response relevance, latency, and hallucination rates to measure the effectiveness of RAG models. - Bias Detection & Mitigation – He provided strategies for detecting and reducing bias in AI-generated content, ensuring that responses are fair and neutral. - Model Transparency & Explainability – Amir highlighted the importance of improving the interpretability of RAG models to enhance user trust and make AI applications more transparent. Attendees gained valuable insights into best practices for evaluating RAG systems and how to apply these techniques to optimize AI applications effectively. This tutorial by Amir Siddiqui was held on April 10, 2025, at DSC MENA 25 ONLINE.