Large Language Models (LLMs) are powerful, but they come with a big limitation: their knowledge is frozen at training time and is limited to what was in the training data. Retrieval-Augmented Generation (RAG) changes that by allowing models to pull in fresh, domain-specific context at query time.
In this talk, we’ll explore how you can build more intelligent and more useful AI systems by combining LLMs with open-source tools for RAG. We’ll cover:
- Why “just prompting harder” or having a longer context isn’t enough.
- The open-source ecosystem: from vector databases to frameworks.
- Practical design choices: chunking, embeddings, retrieval strategies, and evaluation.
At the end of the talk, you’ll have a clear understanding of how to set up your own open-source RAG pipeline and make your LLMs not just bigger, but truly smarter.