You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed everything in a vector DB and do RAG."Interview over!Here's what you missed:Devs treat context retrieval like a weekend project.Their mental model is simple: "Just embed the data, store them in vector DB, and call it a day."This works beautifully for static sources.But the problem is that no real-world workflow ...

Technology & Innovation - Airweave addresses the limitations of RAG (Retrieval-Augmented Generation) in handling real-time data [1] - Airweave constructs live, bi-temporal knowledge bases to ensure agents utilize the most up-to-date information [1] - The system supports comprehensive agentic retrieval, incorporating semantic and keyword search, query expansion, and integration across 30+ sources [1] - Airweave is 100% open-source [1]