Open Claude.ai or any other LLM chat. Paste a Kaggle dataset and ask it to be your tutor. Follow the plan one phase at a time. Get a second opinion at the end to catch what you missed.
The second-opinion session is the insight most people skip. The tutor session can't catch its own blind spots. it's been inside the build since the beginning. A fresh session with no history has no stake in defending earlier decisions. That's not a workflow trick, it's an epistemology.
The TotalCharges leakage example is perfect: the model wasn't predicting churn, it was detecting it after the fact. That distinction only shows up when someone walks in without context.
I write about production AI systems and distributed backends the layer where these models actually run. Worth a subscribe here too.
The second-opinion session is the insight most people skip. The tutor session can't catch its own blind spots. it's been inside the build since the beginning. A fresh session with no history has no stake in defending earlier decisions. That's not a workflow trick, it's an epistemology.
The TotalCharges leakage example is perfect: the model wasn't predicting churn, it was detecting it after the fact. That distinction only shows up when someone walks in without context.
I write about production AI systems and distributed backends the layer where these models actually run. Worth a subscribe here too.