In enterprise AI, the gap between prototype and production is vast. Senior AI Engineer Aakriti Aggarwal of IBM Watson shares how she solved one of the biggest AI challenges at IBM: building Retrieval-Augmented Generation (RAG) systems that process millions of documents while maintaining high accuracy.
Most RAG tutorials focus on a handful of PDFs and simple question-answering. But real-world enterprises often need to handle massive document volumes—thousands of pages each. Aggarwal explains that production AI systems are fundamentally different from demos, where accuracy is critical and reliability at scale is non-negotiable.
"The biggest AI problem I solved at IBM was building enterprise-scale RAG systems that handle massive document volumes while maintaining high accuracy."
Her insights highlight the importance of moving beyond toy examples to understand what it truly takes to deploy AI in business environments. Whether you're learning about generative AI, RAG, LLM applications, vector databases, or AI engineering, her experience offers a glimpse into the real-world challenges of enterprise AI.
For those interested in building industry-ready AI skills, Aggarwal's work underscores the value of understanding how to scale AI solutions properly—an essential lesson for anyone pursuing a career in AI.