RAG + Synthetic Data: The New Blueprint for Building Accurate AI Models

Synthetic data is evolving, and Retrieval-Augmented Generation (RAG) can make it dramatically more accurate and domain-grounded. In this video, I walk through how combining RAG + SDG allows you to generate high-quality, regulation-aware datasets, especially for sensitive fields like healthcare, finance, and legal compliance. Using the ICMR Guidelines as an example, I demonstrate: • How context improves the accuracy of generated datasets • A side-by-side comparison: with vs. without RAG context • When fine-tuning becomes necessary • How RAG-supported SDGs can also create better evaluation datasets This feature is currently in beta on DataCreator AI and will be available soon. If you want more videos on building practical AI systems with synthetic data, fine-tuning, and evaluation workflows—subscribe and leave a comment below! DataCreator AI: https://datacreatorai.com/