AI Generated Code Looked Right, but the Data Was Wrong
An AI Data Analyst was tested using a diabetes dataset, which initially appeared to function correctly. However, upon closer inspection, it was discovered that the data was misaligned, leading to nonsensical values in key columns. The AI's ability to analyze the output helped identify the issue, highlighting the importance of verifying data integrity beyond just executing code.
- ▪The AI generated Python code to load a diabetes dataset from a CSV file without errors.
- ▪Upon reviewing the dataframe, it was found that the values in the Pregnancies and Age columns were incorrect and misaligned.
- ▪The AI detected suspicious statistics and missing values, prompting a deeper analysis of the output.
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May 04 2026 · Piotr Płoński aidata analysisAI Generated Code Looked Right, but the Data Was WrongI'm working on an AI Data Analyst in MLJAR Studio. The idea is simple. You ask a question in natural language, and AI writes Python code, executes it, and shows the result. It should make data analysis faster and easier. But while testing this feature, I found a very interesting example. It reminded me that AI data analysis can't be only about generating code. The code is just one part. The output also needs to be checked. A simple medical data analysis use case I was testing a medical use case. The first step was very simple. I wanted to load a diabetes dataset from a CSV file. So I wrote a short prompt with the URL to the file. The AI generated Python code with Pandas. Nothing special.
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