From Search Results to Insights

GenAI services have been adopted successfully in no time across various digital business models, but what if your data has the better answers? How could this innovative technology be combined with a companies knowledge and data?

In this talk we delve into the intricacies of Large Language Models (LLMs) and their augmentation with custom data through the use of Retrieval-Augmented Generation (RAG). Learn about Statista's pioneering journey in moving from extensive search results to concise and well-founded answers, using their LLM-based application, ResearchAI. We'll tackle the challenges faced, including building a skilled team for such an emerging technology, the impact of exclusive data sources on answer quality, high product costs and latency per request, and the tendency of LLMs to produce hallucinations despite the availability of accurate data.

This session offers a realistic look at the hurdles encountered and the strategies employed, providing valuable lessons on building and optimizing RAG applications in the real world.

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