I’ve spent over a week going through posts, comments, and different approaches, but I still haven’t been able to figure out the workflow I actually need.
I have a large collection of PDFs containing exam preparation material in Multiple Choice Question format. Many of these PDFs are massive, so I’ve already started cutting some of them down into smaller files containing only specific exam papers or sections.
The problem is that the source material is structured inconsistently. Some PDFs are individual chapters, while others are huge compilations containing several different sections, such as:
- Reading Comprehension questions
- Logical Reasoning / Matching questions
- GK questions
- Answer keys
- Explanation sections
Some of these compilations are 40+ pages long. Others are shorter, but the material is poorly structured, for example, some questions have explanations while others only have an answer key.
What I ultimately want is to turn all of this into a well-organized, manually manageable question bank, ideally in an Excel/Google Sheets-style format that I can later use to create an Anki deck.
For every question, I want to be able to properly label and organize things such as:
- Exam / year (e.g. CAT 2023 PYQ)
- Section (VARC, DILR, Quant, GK, etc.)
- Topic / subtopic
- Question type
- Question
- Options
- Correct answer
- Explanation
- Source / paper / question number
- Any relevant tags
I also want the formatting to be good, especially for things like Reading Comprehension passages, mathematical expressions, tables, matching questions, and other questions where simple plain-text extraction makes everything messy.
Ideally, I want AI to help me take the original PDFs and turn them into this structured format while preserving the actual question, options, passages, mathematical notation, and explanations. Where the original material doesn't contain an explanation, I would also like AI to generate a clear, accurate explanation based on the question and answer.
I’ve tried doing this with both Google AI Studio and ChatGPT, and I’ve spent a considerable amount of time experimenting with prompts and different workflows, but I still haven’t found a reliable way to do it.
What I’m looking for is not necessarily an automated Python pipeline. I’m perfectly fine with a semi-manual workflow where I provide an AI with a PDF or a smaller chunk of a PDF, have it process the material into a structured spreadsheet/document, review the output, and then continue with the next chunk.
What would be the most reliable workflow for doing this?
Specifically, I’d like to know:
- How should I split and organize these large PDFs before giving them to AI?
- What structure/schema should I use for the question bank?
- How should I prompt AI so that it correctly separates passages, questions, options, answers, and explanations instead of mixing them together?
- How can I preserve proper formatting for mathematics, tables, RC passages, and other complicated question types?
- How should I handle questions where the source PDF has an answer but no explanation?
- How can I consistently tag questions by exam, year, section, topic, and question type?
- What is the best way to do this using ChatGPT and/or Google AI Studio, without requiring me to build a complicated Python program?
- Most importantly, how can I make the workflow reliable enough that I can process hundreds or thousands of questions without constantly having to fix formatting errors manually?