How to Evaluate RAG Systems with Python (Pandas & Golden Datasets)

Building a retrieval-augmented generation (RAG) pipeline is one thing; proving that it actually works in a production environment is another entirely. In this capstone session, we design and evaluate a complete RAG architecture for a real-world business (a mobile accessories site). We pull down their public policies, chunk the data, embed it into a local ChromaDB vector store, and test the precision of our semantic search. We break down the critical difference between blind character-limit chunking and "Semantic Chunking," exploring why preserving line breaks and paragraph structures prevents catastrophic data loss during embedding. Finally, we write a Python script using Pandas to test our retrieval engine against a "Golden Dataset." We configure a local Llama 3.2 model as a strict judge (Temperature = 0) to compare the retrieved context against our verified ground truths, logging the faithfulness scores to identify exactly where our pipeline hallucinates or fails. Key Takeaways: Semantic Chunking: Blindly slicing a document by token limits destroys context. A smart RAG pipeline chunks text semantically—breaking at natural paragraphs, headers, or line endings to ensure every vector maintains a complete logical thought. Overlap Strategy: When chunking text, you must implement an overlap (e.g., 100 characters). This ensures that a concept split across two chunks still retains enough context to be correctly retrieved during a vector search. Zero-Temperature Auditing: When using an LLM to judge your RAG pipeline's accuracy, you do not want it to be creative. Setting the temperature to 0 forces the model to act deterministically, returning strict numerical ratings instead of rambling justifications.