Semantic search

We’ve all typed the right word into a search box and still come up empty. The problem isn’t our typing—it’s that keyword search doesn’t really get meaning. Semantic search tries to fix that. Instead of just matching words, it looks for ideas.

Vectors are the trick

Artificial intelligence (AI) represents text as vectors—lists of numbers that describe meaning. Once that’s done, she can compare two sentences the same way we compare arrows on a graph: by direction and length. If two arrows point mostly the same way, the meanings are close.

Cosine similarity keeps it simple

Cosine similarity is a rule of thumb for comparing vectors. Forget the math details; think of it as the angle between two arrows. Zero degrees means they’re pointing the same way (high similarity). Ninety degrees means they’re off in different directions (low similarity). We just need to know: smaller angle, stronger match.

Databases built for vectors

Normal databases aren’t made for this. Vector databases are. They can hold millions of these arrows and still find the closest one in milliseconds. That makes it possible to search piles of text, code snippets, or even images by meaning instead of just keywords.

Why it matters

When she runs a semantic search, AI isn’t fooled by synonyms or clumsy phrasing. If we ask about “fixing a login problem,” she can connect it to “authentication error” even if those words don’t appear. It feels more like talking to someone who gets context.

A coder’s thought

We don’t need to worship cosine formulas or vector stores. We just need to remember the point: make search act less like a dictionary lookup and more like a conversation. That’s something even we can get behind.