Enhancing Data Engineering And Knowledge Discovery With Retrieval-Augmented Generative AI

Retrieval-Augmented Generation (RAG) is now a powerful technique to enhance the capabilities of Large Language Models (LLMs) through the use of retrieval based on external knowledge together with generative techniques. The new paradigm of Retrieval-Augmented Generative AI proposed in the current paper concerns the field of intelligent data engineering and knowledge discovery. The suggested system will combine the data ingestion, pre-processing, semantic retrieval, and generative reasoning into one pipeline to improve the data interpretation and generation of insights. The framework can minimize the amount of hallucinations and improve the accuracy using embedding-based retrieval and context-based generation. Experimental assessment of simulated data demonstrates that the proposed model is more accurate, retrieves faster and can be scaled better than traditional LLMs and the existing RAG-based models. The results point to the utility of considering retrieval mechanisms in data engineering operations to assist in a greater amount of knowledge discovery and decision-making.