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.
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.