Searchable Case Study Library for Banking Education

Added Nov 2025 3 design docs

Case studies are one of the best ways to learn banking, since real events such as failed risk controls, successful product launches, and landmark regulatory actions teach lessons that textbooks flatten. The difficulty is discovery: relevant cases are scattered across sources, and a student rarely knows which one matches the topic at hand. The intern builds a streamlit application that makes a case study collection searchable by meaning rather than keyword. Case documents are embedded and stored in a vector database, and when a user asks about a topic such as credit risk in small business lending, a retrieval-augmented generation pipeline pulls the most relevant cases and generative AI produces a concise summary of each one, highlighting the key lessons and outcomes. Everything is written in python, and the interface lets users browse results, read summaries, and drill into the underlying case text. The project is an approachable introduction to RAG: the intern learns how embeddings capture topical similarity, how retrieval grounds generated summaries in real sources, and how to package the whole system as a clean educational tool for banking learners.

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