Beginner-Friendly Banking Concept Explainer with RAG Search

Added Nov 2025 3 design docs

Terms like amortization, repo rate, and tier-one capital form a wall of jargon between newcomers and a working understanding of finance. Generic chatbots can define these terms but often hallucinate details, so a trustworthy explainer needs to ground its answers in a curated body of reference material. The intern builds that explainer as a streamlit application in python. A reference collection of banking definitions and explanatory articles is embedded into a vector database, and when a user types a concept they find confusing, a retrieval-augmented generation pipeline fetches the most relevant passages and generative AI composes a clear, beginner-friendly explanation from them, often with a simple example. Because every answer is grounded through RAG in the curated content, the tool avoids the invented facts that plague ungrounded chatbots, and users can see which sources informed each explanation. The project teaches the core mechanics of retrieval-augmented systems, including chunking, embedding, similarity search, and grounded prompting, while also exercising judgment about explanation quality. The intern finishes with a polished educational tool and a solid grasp of why grounding matters in AI products.

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