Student Builds AI Lecture Chatbot, Discovers It Confidently Spreads Wrong Answers

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A computer science student built a lightweight RAG-style chatbot using Python and a free MonkeyCode server tier to help study seventeen lecture PDFs ahead of a midterm. The tool used TF-IDF cosine similarity to retrieve relevant text chunks and passed them to a language model for plain-language answers, with no vector database or fine-tuning involved. For the first few days the bot performed well, accurately recalling lecture details and slide references. However, when asked whether gradient descent guarantees a global minimum, the bot retrieved only one relevant slide and confidently gave an incorrect answer, omitting a contradicting slide that clarified the nuance for non-convex problems. The experience highlighted a key limitation of retrieval-based AI tools: when retrieval is incomplete, the model can produce authoritative-sounding but factually wrong responses.

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