A mind-numbing pain that I share with many other students is one that, satirically, our University takes immense pride in. We are a top-ranked research school that funnels $1.86 billion annually into research. The Undergraduate Research Opportunity Program (UROP) is seen as a must-have for resumes, but so many of us are just tossed into a lab and expected to know how to research. Professors lighten the load for us by having us start with lighter tasks like literature search, but what happens when reading research papers is that you “read but forgot to understand.” Having the opportunity to create my own Maizey project \u2014 a straightforward process that any student can do in minutes<\/a> \u2014 I decided to create something that would help bridge the gap between seasoned researchers and overwhelmed college students. A chatbot for researchers.<\/p>\n<\/div>\n\n\n\n Maizey is a RAG. Not that we wipe tables with, RAG stands for Retrieval-Augmented Generation<\/a>, and is a distinct type of generative AI model. Maizey takes input from a Google Drive folder or Canvas course<\/a>, and can read most text-based file types like .pdf or .docx. So when you ask it a question, Maizey will go to the input source and look for relevant information to append to the query. It then runs that augmented query through a large language model like GPT 4 Turbo, and returns the response to you. This makes Maizey capable of some pretty terrific things<\/a>, as it really is what you make of it.<\/p>\n<\/div>\n<\/div>\n\n\n\n In my attempt to replicate greatness, I fed my Maizey the papers we read for a lab I was a part of and started asking it questions that our lab manager often asked of us. I was elated that it was responding until I asked U-M GPT the same questions. It gave the same, if not a better, answer, meaning that my Maizey did not learn anything from the papers I fed it, which is understandable. I do that, too.<\/p>\n\n\n But I wanted it to be better than me. I then tried to play around with the system prompt, which turned out to be quite the game-changer. I essentially told Maizey to roleplay as a knowledgeable research assistant, explained its capabilities in summarizing literature, explaining methods, identifying variables, etc., and told it to actually read the papers I provide. Then, it started giving pretty specific answers that it would not have known without reading the papers. One great feature about Maizey that works hand-in-hand with research is how it cites the material it retrieved additional information from, so it\u2019s easy to track citations.<\/p>\n\n\n\n But remember how Maizey is text-based? With its current capabilities, Maizey does not deal with graphs, tables, or images that well. While Maizey can breeze through a text-heavy paper, it struggles to correctly return information from graphs. This means that at the time of writing, the efficacy of your research assistant Maizey, depends on the research papers in your field. This is something I will keep working on, as graphs and tables are crucial to papers and can be almost as confusing as text at times. Not to mention the countless times that it would just pretend it never read the papers and say, \u201cI don\u2019t know.\u201d<\/p>\n\n\n![\"An](\"https:\/\/michigan.it.umich.edu\/news\/wp-content\/uploads\/2024\/06\/meme-Joey-Lin.jpg\")
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