Large Language Models are powerful, but they struggle with up-to-date, domain-specific, or private knowledge. Retrieval-Augmented Generation (RAG) is a practical architecture that addresses these limitations by combining information retrieval with language generation.
In this hands-on tutorial, participants will learn how to implement a RAG system entirely in Python using lightweight, open-source libraries. We will focus on architectural decisions that matter in real systems, including document chunking, retrieval strategies, prompt construction, and evaluation tradeoffs.
Participants will incrementally build a Python-based RAG pipeline that:
- Ingests and indexes a document corpus
- Performs retrieval using cosine similarity
- Injects retrieved context into prompts
- Produces grounded, explainable answers
No prior experience with LLMs, vector embeddings, or RAG is required. Attendees only need basic Python familiarity.
By the end of the session, participants will have a working Python RAG prototype, a strong mental model of its components, and the knowledge to extend it with more advanced embeddings or LLMs on their own. This tutorial balances conceptual understanding with hands-on Python implementation, preparing attendees to confidently explore RAG in their own projects.