This workshop-converted-to-talk shows extending a sample movie streaming platform with semantic search, recommendations, and similarity explanation. It demonstrates vector search and LLM integration. While the code you'll see doesn't come from Netflix, it's an example of how embracing relatively simple AI tools yields impressive results.

You'll learn how to chunk text, why combine dense and sparse embeddings, how vector centroids capture user preferences, and what it takes to merge multiple search strategies. You'll also witness the implementation of a RAG pipeline that finds similar movies and explains those similarities.

The presentation will not blow your mind with the next groundbreaking AI achievement, but will show you code you can understand and write yourself. The focus is on making AI features work reliably and delivering results rather than showing off.