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A Quick Intro

A Quick Intro

Welcome to my exploration of Retrieval-Augmented Generation (RAG) evaluation techniques and chunking strategies, capturing my journey to understand how different approaches to document chunking affect RAG system performance.

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Overview

  • Chunking Strategies: Comparing naive approaches (like RecursiveCharacterTextSplitter) with semantic chunking methods that preserve meaning
  • Chunking Strategies Transcript: Audio overview summarizing the experiential journey of building and evaluating RAG pipelines
  • Evaluation with RAGAS: Using specialized metrics like Faithfulness, Answer Relevancy, Context Precision, Context Recall, and Answer Correctness

Key Findings

  • Semantic chunking significantly improves context precision and answer relevancy compared to naive approaches
  • Different chunking strategies are optimal for different types of queries and content
  • Trade-offs exist between processing speed and retrieval quality - semantic chunking improves quality but requires more computational resources
  • Specialized evaluation frameworks like RAGAS provide insights traditional metrics miss, allowing precise measurement of RAG performance

License

This content is available under the MIT License.

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