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Evaluating Embedder and Reranker Model:An Advance RAG Approach
A comprehensive guide on evaluating RAG pipeline performance using LlamaIndex's Retrieval Evaluation module, focusing on Hit Rate and MRR metrics. The article provides hands-on implementation details
for combining different embedding models and rerankers, using the Llama2 paper as a practical example.

Reasons to Read -- Learn:

  • how to quantitatively evaluate RAG systems using two specific metrics (Hit Rate and MRR), enabling you to make data-driven decisions about your retriever's performance
  • how to implement a custom retriever that combines vector search with reranking capabilities, complete with working code examples using LlamaIndex
  • how to generate unbiased question-context pairs using Anthropic's LLM for testing retrieval systems, including detailed implementation steps and best practices
  • 7 min readauthor: Amanatullah
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