With the rapid democratization of LLMs, organizations are discovering novel applications of gen AI that data engineers need to learn to support.

What it takes to build more reliable data and AI systems - and give LLMs the diligence they deserve.

The ELTP framework highlights how integrating updated, specific company data with language models like ChatGPT can overcome their limitations.

He discusses the integration of semantic layers with Large Language Models, envisioning AI's enhanced understanding and interaction with organizational data.

Recognize the vital role data engineering plays in AI.

LLMs are trained on huge volumes of data, they're not trained on *your* data and they can't answer questions about your company.

Starburst's data access layer and strategic approach to data management significantly enhance the performance and applicability of Generative AI and LLMs in various organizational contexts.

Going from synthesis in LLM-based data modeling to a deterministic system with reliable verification and repair.

GPT-4 is powerful enough to write correct SQL in almost any dialect, but what is missing?

Brian will explore the transformation of diverse data forms into AI-ready formats, enhancing generative AI's efficacy in organizational contexts, with a focus on clean JSON data and overcoming LLM limitations.