6d ago · 7 min read · Photo by Brett Sayles on Pexels You're staring at your AI agent's lackluster responses, wondering why it keeps hallucinating facts about your company's products. The truth is, most developers jump straight into building agents without understanding t...
Join discussionApr 15 · 8 min read · Picture this: you've built an AI agent that can handle customer support tickets brilliantly, but it keeps asking the same customer their name and order number in every conversation. Sound familiar? We've all been there — creating agents that work per...
Join discussionApr 1 · 9 min read · Building Persistent AI Agent Memory Systems That Actually Work Photo by Andrey Matveev on Pexels Here's a startling fact: 87% of AI agents fail at multi-turn conversations because they can't remember context beyond their immediate training window. W...
Join discussionFeb 16 · 11 min read · Vector Database Sharding Strategies for Billion-Scale AI When your embedding-powered application grows from thousands to billions of vectors, you'll hit a wall that most teams don't anticipate. A single-node vector database that handled 10 million em...
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Nov 11, 2024 · 21 min read · Traditional vs Vector Databases 1. Traditional vs. Vector Databases - Key Differences Traditional Databases: Handle structured data (rows and columns) best, like in SQL databases. Vector Databases: Specialize in unstructured data (images, text, aud...
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