Entwicklung einer Retrieval Augmented Generation-Anwendung zur Unterstützung bei der Anerkennung von Prüfungsleistungen

A Retrieval‑Augmented Generation (RAG) system is presented to automate the recognition of university credits when students transfer between institutions. The solution combines a Minimal‑Marginal‑Relevance retriever with large language models (Llama‑3.1‑8B‑Instruct and GPT‑4o) and employs query expansion to improve the relevance of retrieved document chunks, with GPT‑4o delivering notably more accurate answers. The application provides a stable, user‑friendly web interface for uploading module handbooks and querying them, and its modular architecture enables future extensions such as a broader knowledge base and full automation of the credit‑recognition workflow.