Operational Database Management Systems
Q1. At Kepler, your team focuses on “separating what AI does well from what code does well” to ensure absolute ground truth. In the context of financial services—where a misplaced decimal point can break a...
Q1. Kartik, you have built massive data infrastructures at both LinkedIn and Pinterest. As we transition from traditional software engineering to “Agentic Engineering,” how do the core competencies of a top-tier engineer change when...
By David Rolfe Some of you will be aware of the concept of a “Black Swan Event”. The term was coined by Nicholas Taleb, and refers to a rare event with significant consequences. Something so...
Stanford Health Care, part of Stanford Medicine, was awarded a 2025 InterSystems Impact Award for its AXIOM initiative for fast access to electronic health records. In turn, AXIOM underpins the ChatEHR medical chatbot that Nikesh...
“Though AI speeds coding up, Devs still need to Master their craft” Q1. Progress is claiming up to 30% productivity gains with the expanded AI coding assistants across Telerik and Kendo UI libraries. From...
Q1. You’ve spent two decades working on decision science systems, and ontologies play a critical role in both data management and AI. Can you discuss how you approach the generation and maintenance of ontologies...
Q1. Emil Eifrem identifies ‘extracting structure from unstructured data‘ as a critical property for AI data layers. How does Neo4j’s approach to LLM-driven entity extraction and relationship discovery differ from traditional NLP pipelines? Can...
Moderated by Ramesh Chitor. Q1. What are the main limitations of Large Language Models (LLMs) when it comes to solving long-horizon or multi-step reasoning tasks?Can you share specific examples where LLMs tend to fail as...
Q1. From a database perspective, what are the main challenges that Generative AI (GenAI) poses? The biggest challenge that Gen AI poses is scale. We have so many sources of data – data used...
Q1. What is Retrieval Augmented Generation (RAG)? Retrieval Augmented Generation (RAG) is a method that combines the capabilities of generative models, like LLMs, with information retrieval techniques. In a RAG system, when presented with...