The code posted by Difficult_Cricket319 is exactly the kind of careful, defensive validation that most spreadsheet projects skip until something breaks. That is a mistake, and prioritizing it is correct. The logic they have built, checking file existence, confirming the file is not locked, verifying it is not empty, and then matching header rows against an expected array, is a solid foundation. But it also reveals a deeper truth about data workflows: validation is never finished, and chasing perfection through an AI chatbot is a recipe for frustration.
The cycle of asking Google AI for suggestions, making changes, and then being told to change something else is described. That loop is not a failure of the code; it is a feature of how general-purpose AI handles specific, context-dependent problems. The AI does not know that File1 comes from SharePoint with a particular header structure, or that File2 is an email attachment with only updated records, or that File3 is a manually created report. It sees generic validation patterns and offers generic advice. Checking with human experts is the right instinct, because validation logic should be driven by the actual data sources and their quirks, not by abstract best practices.
What the author has already written is more robust than they give themselves credit for. The `IsThisCSV` function does not just check the file extension, it reads the first line, strips quotes, splits by comma, and compares every header cell. That catches files that are technically CSV but have the wrong schema. The `IsValidWB` function attempts to open the workbook through the Excel object model, which catches corruption that a simple extension check would miss. The `IsFileOpen` function handles the real-world problem of locked files. These are not trivial checks. They are the kind of defensive programming that prevents downstream errors from propagating through the three-file pipeline.
The practical takeaway for anyone building similar workflows is this: validate the data you actually receive, not the data you expect to receive. Hard-coding the expected header array is a good start, but making it more generic by reading the header from a configuration sheet or a separate reference file would be better. That way, when the SharePoint export changes a column name, the validation breaks in a predictable place rather than silently failing. The same principle applies to the workbook validation, check for the number of sheets, expected sheet names, and even row counts if the data volume is predictable. Validation should be an explicit contract between the file and the code, not a hopeful guess. The logic is already most of the way there. They just need to trust their own logic more than the chatbot's second-guessing.