Retrieval Augmented Generation (RAG) - Programatic Lab
Solution overview
This hands-on Jupyter Notebook guides you through building a complete Retrieval-Augmented Generation (RAG) system from start to finish. You'll begin by loading and chunking your source documents (data preparation). Then, you'll convert these chunks into vector embeddings using a chosen model and index them in a vector store for efficient similarity searching (embedding & retrieval). Finally, you'll learn how to take the relevant document chunks retrieved based on a user query, combine this context with the original query into an effective prompt, and integrate it with a Large Language Model (LLM) to generate an informed, context-aware answer. By the end, you'll have implemented and tested a functional RAG pipeline within the notebook environment.