Are you using AI to handle the heavy lifting of data cleaning and ETL processes yet?

All answers to this question.

I have actually moved a significant portion of our data preprocessing to an AI-driven pipeline. We use a custom model that identifies structural anomalies in incoming JSON files from various vendors. It used to be a nightmare because every vendor has a different format, but the AI is surprisingly good at mapping these to our internal schema with high accuracy. It saves our engineering team about 15 hours of manual mapping scripts every time we onboard a new partner. We still do a weekly sanity check, but the heavy lifting of writing custom parsers for every single new data source is effectively gone.

   Answered 2025-11-12 by Victoria Fletcher

Victoria, when you say it maps with high accuracy, what kind of confidence threshold are you requiring before it merges into production?

   Answered 2025-11-15 by Daniel Sutton

• Daniel, we set a strict 95% confidence score requirement. Anything lower than that gets sent to a manual review queue. We found that the AI handles the standard "clean" cases perfectly, allowing our human experts to focus only on the truly weird edge cases. This hybrid approach keeps our data integrity high while still drastically increasing our overall velocity. It's been much better than the old rule-based systems that broke every time a vendor changed a single column name.

     Commented 2025-11-17 by Victoria Fletcher

I've automated the generation of my documentation. I feed the code into a local LLM and it produces perfect docstrings and README files instantly.

   Answered 2025-11-20 by Kimberly Page

• Documentation is a great use case, Kimberly. It’s usually the task everyone hates most. I’m glad to see people reclaiming their time using these tools.

     Commented 2025-11-21 by Philip Crawford