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Take GenAI to the next level with RAG using vector storage and search
Large Language Models (LLMs) are generally knowledgeable, but they don't know your domain. In this session, David Jones-Gilardi, developer relations engineer at DataStax, will build a visual GenAI workflow powered by a vector database that enables seamless vector storage and search. He'll create a real-time Retrieval Augmented Generation (RAG) pipeline, dynamically enhancing LLMs with domain-specific knowledge. Along the way, he’ll also use GitHub, Codespaces, and GitHub Copilot to create a fully functional GenAI app to showcase an experience that learns as your data grows. See how to level up your GenAI development process using these innovative tools.

, Developer Relations Engineer, DataStax

Session Type: Product Demo
Key Takeaway 1: Learn how to augment LLMs with domain-specific knowledge in real time.
Key Takeaway 2: Gain practical insight into building GenAI workflows with Langflow.
Key Takeaway 3: Discover how to set up and use DataStax Astra DB for seamless vector storage and search.
Topic: AI, Open Source, Software Engineering, Productivity
Target Audience: Open Source Developers or Maintainers, Startups, Educators
Industry: Applicable to all
Level: Level 100: Introductory
GitHub Product: Copilot
Delivery Format: In-person, Recorded, On-demand