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.

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.