Many people drive their cars around all day and never look under the hood. Likewise, search is used every day, millions of times, with very little thought for how those results appear on your device. While I can't build a car engine from scratch (yet), I can build a search engine! (mostly).

In this talk, we'll take a beginner-friendly approach to discussing how search engines work under the hood while building one from scratch in Python along the way. We'll discuss some history of search, demonstrate common data structures that make search engines fast and effective, and review how we can get results in a meaningful order.

We'll also take a high-level look at machine learning models and how vector embeddings can easily understand similarity and influence search results.

This talk is for those interested in taking a beginner's peek under the hood to understand some of the techniques used. No prior experience with information retrieval required—just bring your curiosity about what really happens when you type a query into that search bar.