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Building LLM Applications with Retrieval-Augmented Generation: A Fireside Chat


Join us for an engaging conversation about Large Language Models (LLMs) with a panel of industry leaders: Raja Iqbal (Chief Data Scientist, Data Science Dojo), Taimur Rashid (Chief Business Officer, Redis), Sam Partee (Principal Applied AI Engineer, Redis), and Daniel Svonava (CEO, Superlinked).

In this era where generative AI and LLMs are reshaping industries, businesses must be equipped to harness their potential fully. This discussion will focus on the common design patterns for LLM applications, especially the Retrieval-Augmented Generation (RAG) framework. The speakers will discuss various strategies for embedding knowledge into these models, using vector databases and knowledge graphs in fetching domain-specific data.

This discussion aims to not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also to empower technical architects and engineers with practical insights and methodologies.

Sam Partee-LLMs-Generative AI
Sam Partee

Principal Applied AI Engineer at Redis

Sam Partee is a Principal Engineer who helps guide the direction of AI efforts within Redis. Sam assists customers and partners in deploying Redis in ML pipelines for feature storage, search, and inference workloads. His background is in high performance computing and machine learning systems.
Taimur Rashid-Redis-LLMs-Generative AI
Taimur Rashid

Chief Business Officer, Artificial Intelligence Business at Redis

Taimur Rashid is the Chief Business Development Officer at Redis Labs, leading corporate strategy and emerging businesses with a specific focus on AI/ML. Before Redis Labs, Taimur was General Manager and Head of Customer Success at Microsoft. Taimur has 20+ years of experience in new market development, strategic business development, and customer success.
Daniel Svonava-Superlinked-LLMs-Generative AI
Daniel Svonava

CEO at Superlinked

Daniel is the CEO at Superlinked – a compute engine that turns data into vector embeddings, powering the next generation of information retrieval for Recommender Systems, Generative AI and beyond. Previously, Daniel was a tech lead at YouTube where he built user modeling and ad performance prediction systems that guide the purchase process for $10B worth of YouTube ads every year.
Raja Iqbal-Data Science Dojo
Raja Iqbal

CEO and Chief Data Scientist at Data Science Dojo

Raja Iqbal is a data scientist, a passionate educator, and an internationally recognized speaker on data science. Prior to Data Science Dojo, Raja worked at Microsoft in a variety of research and development roles involving machine learning and data mining at very large scale.

We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!