§Blog · 6 min read
Can ChatGPT review a Power BI file? (Tested)
The short answer: yes, for a single formula. No, for your whole model. Here is exactly what we found when we put it to the test.
What we actually tested
We took a real .pbix file, a 47-table model from a tier-one metals and mining client, and tried two things: first, we pasted individual DAX measures into ChatGPT and asked for explanations. Second, we uploaded the .pbix and asked for a full audit.
The first test worked well. The second test could not work at all, and understanding why matters if you manage a BI environment.
Where ChatGPT genuinely helps
Paste a DAX measure into ChatGPT and ask it to explain what it does. You will get a clear, readable walkthrough. Ask it to spot a logic error in that measure. It will usually find it. Ask it to rewrite the measure to avoid context transition. It handles that too.
This is real value. If you have one formula that is behaving strangely and you want a second opinion fast, ChatGPT is a solid tool. It is trained on enormous amounts of DAX documentation and forum answers, and for isolated, text-pasted problems it performs well.
Where it fails completely
A .pbix file is a binary. ChatGPT cannot open it. When we uploaded the file directly, the model either refused outright or extracted a thin slice of metadata and worked from that, without telling us how little it could actually see.
Even if you could somehow convert the file to text and paste it all in, you would hit a more fundamental problem: the questions that actually matter in a model audit are not questions you know to ask yet.
- Which of your 400 measures are never referenced by any visual?
- Which relationships have ambiguous cross-filter directions that will cause wrong numbers in specific slicer combinations?
- Which RLS roles have gaps that let the wrong user see the wrong rows?
- Where are the calculated columns that are silently blowing out your model size?
- Which measures use CALCULATE with filters that contradict the row context they are called in?
You cannot paste these questions into ChatGPT because you do not know which specific measures, tables, and relationships are the problem. The whole point of an audit is to find the things you did not know to look for.
The "question you already knew to ask" problem
This is the core limitation. ChatGPT is an excellent tool for answering questions you can articulate. A BI audit is useful precisely because it answers questions you have not articulated, across a scope you cannot hold in your head all at once.
When you paste a measure into ChatGPT, you have already done the triage. You already suspect that measure. You already know which table it lives in and what it is supposed to do. ChatGPT helps you confirm or fix the thing you already found.
What ChatGPT cannot do is read your entire model, trace every dependency, check every relationship, and give you a ranked list of what is wrong in order of business risk. That requires actually opening the file and scanning the whole thing.
How Nexlytics approaches this differently
Nexlytics is browser-only. You upload the .pbix, and within about 90 seconds five AI agents have scanned the whole model: the measure tree, table relationships, RLS configuration, calculated columns, and report-layer dependencies. Nothing to install.
What comes back is a health score, a ranked list of issues with risk context explaining why each one matters, and a client-ready audit PDF you can hand to a stakeholder or attach to a remediation ticket. The agents are looking at the whole file, not just the measure you thought to ask about.
You could spend two clear weeks working through a model of that size manually. Most people managing a BI environment do not have two clear weeks. The $9 trial is there so you can run it on a file today and see what it finds before deciding anything else.
The honest summary
Use ChatGPT to understand a formula you have already found. It is genuinely good at that. Use a proper audit tool when you need a verdict on the whole file, specifically because it will find the things you did not think to look for.
These are not competing tools. They solve different problems. The mistake is using the formula-explainer as a substitute for the model audit.