Vector embeddings have been an Overton window shifting experience for me, not because they’re sufficiently advanced technology indistinguishable fro

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2024-04-17 17:30:06

Vector embeddings have been an Overton window shifting experience for me, not because they’re sufficiently advanced technology indistinguishable from magic, but the opposite. Once I started using them, it felt obvious that this was what the search experience was always supposed to be: less “How did you do that?” and more mundanely, “Why isn’t this everywhere?”

This feels like the right place to start if you’re an app developer looking for an excuse to dip your toes into this new AI world. Embeddings are just arrays of numbers, but they contain a compressed form of a considerable amount of human knowledge and shrink features that used to be substantial specialized projects into ones that individual product engineers can take on.

There are a ton of tooling options available to use embeddings. I’ll highlight our choices and note where you might want to make different ones for your situation. Here are some points I hope you take away:

Vector embeddings work for search and recommendations because they’re good at measuring similarity to arbitrary input. This even works for different spoken languages like French or Japanese.