72% -> 75% -> 92%: a reproducible RAG validation

1. 1Build a knowledge base from a source document
2. 2Convert the document into Q&A pairs using a generic prompt
3. 3Run retrieval tests against the knowledge base using the Q&A pairs
4. 4Refine the Q&A conversion process using a retrieval-aware prompt
5. 5Test and validate the results to measure improvement

Data scientists and AI engineers can benefit from this technique to enhance their knowledge base retrieval systems, improving overall model performance

💡 RAG validation with retrieval-aware prompts can significantly improve knowledge base retrieval accuracy

🚀 Boost retrieval accuracy from 72% to 92% with RAG validation and Q&A conversion! 💡