1. Background
   Language models have broad general knowledge but no access to private or specialised documents, such as institutional regulations, course material or project documentation. When asked about such documents, a model may produce answers that sound convincing but are false.

Retrieval-Augmented Generation (RAG) addresses this. The application first retrieves the passages of a document most relevant to a question, then gives only those passages to the model and instructs it to answer from them, with citations.

2. Objective
   Using the starter kit, build a RAG application with Google’s open-weight Gemma 4 model for one of the two problem statements below. The application must answer accurately from the provided documents, cite its sources, and decline questions the documents do not cover.