Dev Conference by Neo4j
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Session Track: AI
Session Time:
Session description
Modern businesses have lots of diverse data for making decisions. However, the complexity of analysing this data can make it practically useless for decision-makers. How can we make querying complex data more accessible? This talk explores the cutting-edge capabilities of large language models (LLMs) in translating user requests into Cypher and SQL. We assess such factors as dataset complexity, query intricacy, and the impact of few-shot examples on query quality. Which LLM does a better translator job? Elena and Rinat will present cases from multiple industries and introduce an LLM benchmark to pick the best language model for your needs.
Data Scientist, X-INTEGRATE
With completed studies in European Business and in Mathematics, I thrive to make intelligent applications that yield Business benefits. Graph-based applications with Neo4j are a very exciting and promising field that I love working in.
Technical Consultant
I'm a technical consultant. I help to build ML-driven products. I have been working in commercial projects since 2003. Throughout my career, I have been involved in various domains from warehouse management to social platforms to international logistics. This exposure allows me to understand business problems across domains. Helping to set up offices for 4 different companies gave me a deeper insight into the challenges of organisational dynamics, leadership and building healthy teams. I have worked with software startups for most of my career. This created a strong connection with product development, a focus on value delivery and adaptation to changing market conditions. These days, I'm focused on Machine Learning. I help companies to build and ship ML-driven software products.
