From RAG to Real Impact: Building Smarter AI Assistants

Retrieval-Augmented Generation (RAG) is transforming AI assistants from generic chatbots into trusted enterprise partners by grounding responses in relevant organizational knowledge. By combining large language models with intelligent retrieval, RAG improves accuracy, reduces hallucinations, and delivers context-aware answers that users can trust.In this closing session, I will share IBM Bob’s RAG journey, covering the architecture, key design decisions, implementation learnings, and the challenges we encountered along the way. I will discuss how RAG is helping IBM clients accelerate application understanding, modernization, and developer productivity, and conclude with a call to action on how developers can move beyond simply consuming AI outputs and start building AI assistants that are deeply connected to their knowledge, workflows, and business outcomes