Building advanced RAGs with Elasticsearch \ OpenSearch

RAG (Retrieval Augmented Generation) is built in 3 steps - understanding the query, executing a retrieval step and and generating a response. Each step has it's own unique characteristics and challenges. In this session we'll go through the common pitfalls (and solutions!) for each, and demonstrate how you can easily build an efficient RAG using Elasticsearch or OpenSearch.

WTF Are Embeddings

Embeddings are behind the magic of modern AI - powering search, recommendations, and those eerily accurate chatbots.

But what are they, really?

In this talk, Liza - a regular software engineer, not a data science PhD - breaks it down in plain English using real examples, bad charts, and trusty sciency Python tools.

You'll learn how words, products, or even bananas🍌become vectors in high-dimensional space - and why that matters when building systems like RAG pipelines or semantic search.

Whether you're trying to make your chatbot less clueless or your search results less embarrassing, this crash course will give you the mental model you didn’t know you needed.

Delivered at PyCon IL 2025 and OpenSearch community meetup.
For software engineers getting into AI.