GenAI services have been rapidly integrated into various digital business models, but what if your data holds better answers? How can this technology be combined with an organization's knowledge and data?

This talk explores Large Language Models (LLMs) and their augmentation with custom data via Retrieval-Augmented Generation (RAG). Discover Statista's pioneering journey from rich search results to concise, informed answers with their LLM-based application, ResearchAI. We'll discuss challenges such as building a skilled team, the impact of exclusive data on answer quality, high costs, query latency, and LLM hallucinations despite accurate data. This session provides a realistic look at the hurdles and strategies for optimizing RAG applications in the real world.

Target Audience: Data Scientists, Data Engineers, Developers, Decision Makers
Prerequisites: Basic knowledge of GenAI
Level: Expert