The Day My Lecture Notes Bot Contradicted Itself

I was up at 2 AM, staring at seventeen PDFs that refused to tell me anything. My midterm was in six days, and my notes were a mess of arrows, acronyms, and half-typed definitions. I wanted a chatbot that could answer questions about my own lectures. Not a fancy one. Just something that would take a question, find the relevant slide, and answer in plain language. So I built one. I used MonkeyCode for the free model access and free server space. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Their open-source platform's free tier includes 10 million tokens and a server slot, which is enough for a weekend prototype. The “why not” won. The plan was simple: extract text from the PDFs, split it into chunks, retrieve the most relevant chunks with a dumb similarity search, then ask a model to answer from those chunks. No vector database. No fine-tuning. Just a few lines of Python and a POST request. The extraction step was almost too easy. from pypdf import PdfReader def extract_pdf ( path ): return " \n " . join ( page . extract_text () for page in PdfReader ( path ). pages ) Most of my slides were text-heavy, so it worked. One deck came out as garbage because the pages were rotated. That was my first warning: garbage in, confident nonsense out. Next, chunking. I set a chunk size of 1,200 characters with an overlap of a hundred. Small enough to be relevant, big enough to contain a complete idea. def chunk_text ( text , size = 1200 , overlap = 100 ): chunks = [] for i in range ( 0 , len ( text ), size - overlap ): chunks . append ( text [ i : i + size ]) return chunks I didn't use a vector database. My whole corpus was about two hundred chunks, so TF-IDF plus cosine similarity was enough. More importantly, it made every retrieval transparent. I could see exactly which chunks the bot pulled, and why. from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity def retrieve ( query , chunks