Improving accuracy of GenAI apps with Azure Database for PostgreSQL

Tuesday, November 19
5:30 PM - 6:15 PM Greenwich Mean Time
Duration 45 minutes
BRK190
On Demand
Breakout
In Chicago + Online

The success of GenAI apps is decided by the accuracy of their responses. Using Retrieval Augmented Generation (RAG), you can improve accuracy by grounding GenAI app responses in your data. In this session, explore advanced RAG techniques in Azure Database for PostgreSQL including new vector search algorithms, parameter tuning, hybrid search, semantic ranking, and the GraphRAG approach. See how customers are using these techniques to deploy corporate development platform for GenAI apps.

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Maxim Lukiyanov

Principal PM Manager
Maxim is leading Generative AI & Developer Experience PM team in Postgres on Azure service. He has extensive experience in building Data and AI cloud services and has been with Microsoft for 15 years.
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Orhun Oezbek

Software Engineer
Orhun Özbek is the GenAI Lead and a software engineer at UBS, specializing in building reusable and scalable GenAI platforms and providing business solutions for banking AI use cases. He leads the UBS RiskLab Model Services GenAI team which develops the RiskLab AI Common Ecosystem platform which streamlines AI development and deployment across UBS through innovative RAG, AI Agents, Benchmarking and Guardrail functionalities.
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Jay Yang

Executive Director, RiskLab
Jay Yang is an Executive Director and Distinguished Engineer working for UBS. He leads the UBS RiskLab Model Service team which manages the largest data science lab within UBS serving more than 1,200 data scientists and ML/AI developers.