Research Assistant Agent for Student Literature Reviews
University and college students routinely face reading lists that outpace the hours available to them: dozens of papers, articles, and textbook chapters must be located, understood, and connected before an essay or thesis chapter can even begin. Most students triage badly, skimming the wrong sources and missing the ones that matter, because they have no systematic way to search, rank, and digest academic material.
In this project the intern builds a research agent that does that triage for them. The system is a Python service exposed through FastAPI, with LlamaIndex used to ingest and index articles, lecture notes, and PDFs into a queryable knowledge base. OpenAI models sit on top of that index to produce faithful summaries of individual articles, answer follow-up questions grounded in the indexed text, and generate learning recommendations that point the student toward the next most useful source given what they have already read. The intern designs the ingestion flow, the summarization endpoints, and a recommendation route that reasons over the student's reading history.
Completing the project demonstrates practical retrieval-augmented generation skills: document indexing, prompt design for summarization, grounding model output in source material, and packaging it all behind a clean, documented REST API that a front-end team could consume immediately.
