May 7
Loading video player...
Large Language Models are impressive — until they confidently tell you something wrong. Hallucinations, stale knowledge, and factual drift are baked into the nature of generative AI. The fix isn't better prompting. It's memory. Vector databases have quietly become the backbone of production-grade AI systems, acting as a persistent, queryable knowledge layer that keeps AI grounded in facts. But most introductions stop at "they store embeddings." What's actually happening under the hood is far more interesting. It All Starts with Embeddings When you feed text, images, or audio into a vector database, the data gets compressed into a mathematical representation called an embedding — a list of numbers that captures the semantic meaning of the content. Similar things end up numerically close together in this high-dimensional space. When you run a query, it gets vectorized into the same space. The database's job is to find the vectors nearest to yours. Simple in principle. Brutally expensive at scale. Comparing your query against every single stored vector — a brute-force k-Nearest Neighbor (kNN) search — becomes untenable the moment you're dealing with millions of records. This is where the real engineering begins. Speed Through Approximation: ANN Algorithms Production vector databases don't do exact search. They do approximate search — and that trade-off is what makes billion-scale retrieval possible. Two families of indexing structures dominate the space: Inverted File Indexes (IVF) partition the vector space into clusters around centroids. At query time, only the most relevant clusters get searched, dramatically shrinking the search space. Graph-based indexes like HNSW (Hierarchical Navigable Small World) and Vamana take a different approach — they construct layered, navigable graphs that allow for fast traversal toward nearest neighbors. Vamana, the algorithm behind DiskANN, is particularly notable for being optimized to run efficiently directly on SSD storage rather than requiring everything in RAM.