Tired of spending weeks manually creating question-answer pairs for RAG evaluation?
In this episode of RAG Masters, we unveil a game-changing approach to automated QA pair generation that saves you countless hours of manual work.
Neil Katz and Daniel Warfield break down how to leverage LLMs + human-in-the-loop to create high-quality test sets in seconds instead of days. Perfect for RAG engineers, ML practitioners, and anyone building production-grade retrieval systems! 🚀
Tech stack revealed:
-Ground X for document parsing
-OpenAI for QA generation
-Parallel processing for speed
-Visual validation tools
-Automated test set creation
Code on GitHub: github.com/groundxai/code-samples/blob/master/notebooks/RAGMasters_QAGenWithHuman.ipynb
Timestamps:
0:00 - The RAG Testing Problem
2:14 - Why QA Pair Generation is Crucial (and Hard)
4:32 - Introducing Automated QA Generation
7:15 - Visual Demo: Testing Real Documents
11:43 - How the System Works Under the Hood
15:20 - Configuring for Different Document Types
18:45 - Processing Pipeline Deep Dive
22:30 - Implementation Details & Code Walkthrough
26:15 - Output Formats & Integration Tips
28:40 - Wrap-up & Code Availability
#RAGMasters #LLM #AIEngineering #MachineLearning #RAG #Testing #AITesting #datascience