It is entirely possible to turn a messy text file of student loan data into a clean, actionable workbook. The real question is not whether it can be done, but how to approach it without getting stuck in an endless loop of revisions. This user has already learned that lesson the hard way, trying eight versions of a ChatGPT prompt that refused to pull data after a file upload. That is not a failure of will. It is a failure of method.
The problem here is a familiar one: expecting a general-purpose AI chatbot to behave like a purpose-built data tool. ChatGPT is remarkable at generating text and code, but it does not read a text file the way a spreadsheet engine does. It may attempt to parse the content, but it often hallucinates structure or simply fails to extract rows and columns reliably. The user is not asking for a poem or a summary. They want a workbook that organizes every relevant detail from a loan document into a clear, usable format. That is a data transformation task, not a writing task. The tool needs to match the job.
A better starting point is to work directly in an environment designed for structured data. Import the text file into a spreadsheet application and use its built-in tools to split text by delimiters, remove extraneous whitespace, and define columns for amounts, dates, interest rates, and servicer names. From there, formulas or a simple script can flag overdue payments, calculate totals, and highlight the highest-cost loans. The user does not need a custom AI agent for this. They need a few deliberate steps: clean the raw data, define the schema, and let the spreadsheet do the heavy lifting. If automation is desired, a lightweight script in Python or even a macro can handle repeated imports, but the core logic belongs in the spreadsheet itself.
What this user really wants is control and clarity. They have been chasing a magic prompt when what they need is a practical workflow. Start with the raw file in a new sheet. Use text-to-columns or a quick regex find-and-replace to standardize the entries. Then build a summary table that shows total balance, monthly payment, and next due date for each loan. That is the actionable part. Once the structure exists, it can be reused every month with a fresh text file. The eighth version of any prompt is a sign to change the approach, not to try a ninth.