Do You Need A Vector Database in 2026? (ft Arjun Patel)

<p>Join the Tool Use Discord: https://discord.gg/PnEGyXpjaX</p><p><br></p><p>Vector databases can be an important component for building reliable AI agents and scalable semantic search applications. In this episode, Arjun Patel from Pinecone breaks down how to optimize your RAG pipeline, choose the right embedding models (sparse vs. dense), and implement effective chunking strategies for better data retrieval. We also explore the new Pinecone plugin for Claude Code, demonstrating how to build a recommendation system and chat with your documents using Pinecone Assistant without writing complex code.</p><p><br></p><p>https://www.pinecone.io/ </p><p>https://www.linkedin.com/in/arjunkirtipatel/</p><p><br></p><p>Connect with us  </p><p>https://x.com/ToolUsePodcast </p><p>https://x.com/MikeBirdTech </p><p><br></p><p>00:00:00 - Intro </p><p>00:01:11 - What Vector Databases Unlock </p><p>00:04:40 - Optimal Chunking Strategies for RAG </p><p>00:09:07 - How Embedding Models Work </p><p>00:17:25 - Improving Search with Re-ranking </p><p>00:26:52 - SQL vs Vector Database Architecture </p><p>00:35:48 - Claude Code &amp; Pinecone Assistant Demo</p><p><br></p><p>Subscribe for more insights on AI tools, productivity, and vector databases.</p><p><br></p><p>Tool Use is a weekly conversation with the top AI experts.</p>