How Vector Databases Power Modern AI

Описание: How do AI systems find the right information even when the words don’t match?

In this video, we explain how vector databases store meaning as geometry, allowing modern AI systems to retrieve relevant context at speed and scale. You’ll learn how embeddings turn text, images, and audio into vectors, how similarity search works in high-dimensional space, and why traditional keyword databases fall short for AI workloads.

We walk through the full retrieval pipeline—from embedding generation to indexing, approximate nearest neighbor search, and ranking—and show how vector databases enable semantic search, recommendations, and retrieval-augmented generation. Along the way, we cover real engineering tradeoffs: recall versus latency, vector size, the curse of dimensionality, hybrid filtering, and why vector databases complement rather than replace relational systems.

If you want a clear mental model for how modern AI systems actually retrieve knowledge—without hype or hand-waving—this video gives you the foundation that makes everything else click.

This video is part of Enginerds Fundamentals: calm, clear explanations of the systems shaping modern AI, software, and infrastructure.

CHAPTERS:
0:00 – Introduction
1:30 – The Geometry of Knowledge
3:00 – Scaling to Billions
4:31 – End-to-End Retrieval Pipeline
6:01 – When to Use Vector Databases
7:30 – Curse of Dimensionality
9:01 – Balancing Recall and Security
10:31 – Thresholds and Fallbacks
12:01 – Optimal Vector Size Matters
13:31 – Evaluating Search Systems
15:01 – The Power of Embeddings

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