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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| Original publisher | MLJAR |
| Canonical URL | https://mljar.com/blog/ai-generated-code-looked-right-data-was-wrong/ |
| Publication time | Thu, 21 May 2026 11:21:49 +0000 |
| Retrieval time | 2026-05-21T11:36:11.791Z |
| Last seen | 2026-05-21T11:36:11.791Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | g9bT8kTV1TNA |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at MLJAR.
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