AI & Data

Join us for an immersive, hands-on workshop exploring the mechanics of vector search and semantic retrieval. This session goes beyond simply wiring a vector store into a pipeline — it teaches you to understand why certain queries succeed, why others fail, and which components are responsible. Through four focused Jupyter Notebooks, you will explore how embedding models, distance metrics, and chunking strategies each shape retrieval results. Whether you are building retrieval-augmented generation (RAG) pipelines or tuning search applications, this lab equips you with the diagnostic skills and evaluation frameworks to improve retrieval quality on your own data. All experiments run locally using open-source models and FAISS — no prior vector search experience required.