About the talk

Can generative AI benefit from structured content? What role play knowledge graphs in this? And what does all of that have to do with DITA? After they discovered the shortcomings of vector-based RAG there is a buzz in the industry around knowledge graph-driven retrieval-augmented generation (Graph RAG). This session will put the question of whether and how DITA fits into that equation. Michael Iantosca and Helmut Nagy demonstrate a full-scale implementation of a graph-driven RAG based on intelligent structured content. Presented by Iantosca as graph AI theory, just as generative AI blasted into the mainstream, this session explains how DITA was used to automate the construction and maintenance of a scalable knowledge graph to drive generative AI applications along with a fully functional advanced neuro-symbolic chatbot that supports what other models lack: the ability to do inferencing and reasoning. Not a show-and-tell session, but an in-depth review of how the model and solution were built, the theory behind it, and how other teams can replicate it. Several years ago, some audacious Markdown maven declared that DITA was getting “long in the tooth”. Now, at the dawn of neuro-symbolic generative AI, it turns out that DITA was indeed, the future.
 

About the speaker

Michael Iantosca is the Senior Director of Content Platforms at Avalara Inc. Michael spent 38 of his 43 years at IBM as a ‘content pioneer’ leading the design and development of advanced content management systems and technology that began at the very dawn of the structured content revolution in the early 80s. Dual trained as a content professional and systems engineer, he led the charge building some of the earliest content platforms based SGML and DITA and formed the team at IBM that invented DITA.