# Vector Search, Visualised

> Vector search has no EXPLAIN. Through interactive visualisations, this talk shows what embeddings look like in high-dimensional space, what quantisation does to your recall, and how to catch retrieval failures before your agents do.

Author: Simon Hearne (https://simonhearne.com/about/)
Published: 2026-06-04
Canonical: https://simonhearne.com/2026/visualising-vector-search/
Tags: VectorDB, Zilliz, Milvus, talk, video
Video: https://www.youtube.com/watch?v=kY-il0pWjdY
Slides: https://talks.simonhearne.com/vector-search-visualised/

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## About the talk

SQL makes sense. But when it breaks, you reach for `EXPLAIN`. Vector search offers no such comfort. Multi-thousand-dimension embeddings, approximate nearest-neighbour indexes, and quantisation tradeoffs make it hard to know what your system is doing, and harder still to diagnose when results quietly degrade.

Through interactive visualisations, this talk shows what embeddings look like in high-dimensional space, what quantisation does to your recall, and how to catch retrieval failures before your agents do. You'll leave with a sharper mental model and a diagnostic toolkit for the production problems hardest to see.

## What's covered

- **Why vector search exists.** Keyword search matches tokens, vector search matches meaning. How models turn text, images and faces into numbers, and what "similar" really means.
- **Exact vs. approximate search.** Why flat search doesn't scale, the trade-off triangle of speed, accuracy and cost, and how recall and precision measure what you give up.
- **ANN indexes, visualised.** IVF partitions the space, HNSW navigates a graph, DiskANN reaches past RAM. Each with its tuning knobs and where it lands on the triangle.
- **Quantisation and dimensionality reduction.** Scalar, product and RaBitQ quantisation, PCA and Matryoshka embeddings, what each costs you in recall, and how refinement claws precision back.
- **Filters and silent failure.** Why filtering quietly wrecks recall, three ways out by selectivity, and the signals of degradation that your agent will never report.
- **The EXPLAIN you don't get.** How to measure what you can't see, and strategies that actually work in production.

Presented at [TiDB SCaiLE Europe 2026](https://www.pingcap.com/blog/tidb-scaile-europe-2026/) in Stockholm on 4 June 2026 as "When EXPLAIN Isn't Enough: Visualising Vector Search for Engineering and Product Teams".