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Training Script Audit AI Prompt

This prompt reviews an ML training script for correctness, reproducibility, evaluation integrity, and operational training hygiene. It is useful for catching common but costly issues such as data leakage, nondeterministic splits, weak validation setup, and inefficient training defaults before a model is trusted or scaled.

Prompt text
Audit this ML training script for correctness and best practices.

Check for the following issues and flag each as Critical / Warning / Info:

1. Data leakage:
   - Is preprocessing (scaling, encoding, imputation) fitted on training data only, then applied to val/test?
   - Are any features derived from the target variable?
   - For time series: is there any forward-looking data in the features?

2. Reproducibility:
   - Are random seeds set for: Python random, NumPy, PyTorch/TensorFlow, and CUDA?
   - Is the dataset split deterministic?

3. Evaluation correctness:
   - Is the evaluation metric appropriate for the problem type and class imbalance?
   - Is evaluation done on a truly held-out set, never used during training or tuning?

4. Training hygiene:
   - Is learning rate scheduling used?
   - Is gradient clipping applied for RNNs or transformers?
   - Are validation metrics logged per epoch, not just at the end?

5. Resource efficiency:
   - Is DataLoader using num_workers > 0 and pin_memory=True?
   - Is mixed precision (torch.cuda.amp) enabled?
   - Are unused tensors detached from the computation graph during validation?

Return: issue list with severity, line references where possible, and fix recommendations.

When to use this prompt

Use case 01

when you want a structured audit of a PyTorch or TensorFlow training script

Use case 02

when you suspect leakage, reproducibility gaps, or incorrect evaluation logic

Use case 03

when preparing a training pipeline for code review or productionization

Use case 04

when you need issue severity, line references, and concrete fixes

What the AI should return

An audit report listing critical, warning, and info issues in the training script, with line references where possible and recommended fixes.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Training Pipelines.

Frequently asked questions

What does the Training Script Audit prompt do?+

It gives you a structured training pipelines starting point for ml engineer work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for ml engineer workflows and marked as beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Training Script Audit is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Custom Loss Function, Dataset Pipeline Builder, Distributed Training Setup.