))
Fireside Chat with Data Science and AI Experts: Building Large Language Models Applications with Retrieval-Augmented Generation
Serie archiviate ("Feed non attivo" status)
When?
This feed was archived on December 23, 2023 16:12 (
Why? Feed non attivo status. I nostri server non sono riusciti a recuperare un feed valido per un periodo prolungato.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 383827057 series 3528763
Contenuto fornito da Data Science Dojo. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Data Science Dojo o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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.
Un episodio
Condividi
