“Summarize this file” feels like I’m a caveman pounding on the keyboard, expecting AI output excellence, and AI isn’t responding any better.

TL;DR

AI is powerful, but not infallible—especially when analyzing Excel or SQL files. The trick isn’t just uploading a file and hoping for the best. It’s guiding the AI with clear, structured prompts so it sees what you see. In the world of big data, even a slight misunderstanding can lead to a significant mistake.

This week, I’ve hit AI prompt fatigue, struggling to find quick answers in large data sets. Whether it was requesting a clean list of database field names in a database definition file, or hoping AI would figure out how to give me summary defect metrics from a CSV file, the results were flat out wrong.

This wasn’t the first time I’d seen artificial intelligence—supposedly our intelligent new assistant—make a fool of itself with data. And it won’t be the last.

The Day Excel Fought Back

Here’s the thing about AI and spreadsheets: they speak different languages. You see a beautifully formatted quarterly report with merged headers, color-coded sections, and intuitive layouts. AI sees a chaotic jumble of cells where “Q1 Revenue” might span three columns, hidden formulas lurk in plain sight, and dates could be formatted as text, numbers, or actual dates depending on whoever built the sheet.

I learned this the hard way when analyzing a list of defects. The AI confidently told me we had 57 defects in the dataset. Not bad! Except it had created a pivot table and knew there were over 100 defects that I was tracking.

The worst part? AI doesn’t just get things wrong—it gets them wrong with complete confidence. No hedging, no “I might be misreading this.” Just pure, unwarranted certainty that can derail executive reporting.

When Databases Become Word Salad

If Excel files are tricky, database schemas are AI’s kryptonite. When a coworker asked me to help them get a list of database field names, I quickly jumped into action (always the helper)! They sent me the file, which I then dropped into my editor, and I asked for a list of database column names. The first table was short, and it generated a pretty decent list of fields. The second (much larger table) didn’t fare so well. It seemed to produce a cleaned-up list, but my co-worker’s review of AI’s work revealed it was off!

It wasn’t the type of jump-up-off-the-page type of error. The list seemed pretty accurate. It was the kind of error that slips past a quick review and causes problems weeks later when someone builds a report based on the AI’s hallucinated schema.

The Anatomy of AI Overconfidence

Why does this happen? Because AI models are pattern-matching machines trained on millions of examples, but they don’t actually understand your business context. They see customer_id, cust_id, and user_id in different tables and have to guess whether these refer to the same entity or different ones. Sometimes they guess right. Sometimes they don’t.

It’s like asking someone who’s never worked at your company to analyze your internal reports. They might recognize the general structure of a profit-and-loss statement. However, they won’t know that “Special Projects” is actually code for “failed initiatives we’re still paying for” or that the Marketing department splits its budget across three different line items for historical reasons that nobody remembers.

The Art of AI Whispering

The solution isn’t to abandon AI—it’s to learn how to talk to it properly. Think of it as managing a very smart but context-blind intern. You wouldn’t hand them a complex spreadsheet and say “figure it out.” You’d give them specific instructions.

Instead of “analyze this data,” I now say something like: “This Excel file has merged headers in rows 1-2. The actual data starts in row 3. Column C contains dates in MM/DD/YYYY format, and columns F-H are calculated fields showing revenue, costs, and profit. Please first confirm you can identify these structural elements, then provide a summary.”

The difference is remarkable. When I’m explicit about structure and context, AI goes from confidently wrong to actually helpful. When I ask it to verify its understanding before proceeding, it catches many of its own mistakes.

The Trust But Verify Revolution

Here’s what I’ve learned from months of AI-assisted analysis: the technology is incredibly powerful, but it needs guardrails. The best results come from treating AI as a smart but literal-minded assistant that needs clear instructions and constant supervision.

For Excel files, I now always specify the structure upfront. For database schemas, I provide context about naming conventions and business logic. For any analysis that matters, I ask the AI to walk me through its reasoning before delivering results.

The goal isn’t to replace human judgment—it’s to augment it. AI can process data faster than any human, but it can’t replace the business context and sanity checks that can make your career.

And honestly? That caveman AI prompt fatigue mistake turned into an opportunity to help others learn. Going forward, when evangelizing the strength of AI, I’m going to tell them about the time I embarrassed myself by sending a co-worker a quick AI output. Because in data analysis, just like everywhere else, if something seems too good to be true, it probably got misread by an overconfident algorithm.

The future of data analysis isn’t about trusting AI blindly—it’s about learning to work with it intelligently. And that starts with writing better prompts, asking better questions, and never forgetting that behind every smart algorithm is a pattern-matching system that occasionally mistakes your defect list for something completely different.

-Pete