Machine learning development involves a lot of repetitive boilerplate that has nothing to do with the interesting parts of the problem you are trying to solve. Setting up data pipelines, configuring training loops, implementing standard callbacks, writing evaluation functions, handling class imbalance, managing checkpointing: none of this is novel, but it all has to be done correctly before you can focus on model architecture or feature engineering.
Gemini 3 Pro handles this work. Given the right prompt, it generates production-quality Python code for PyTorch and TensorFlow that you can drop directly into your project, audit for correctness, and customize as needed. This guide provides 10 battle-tested prompts that cover the ML development workflow from data loading to model evaluation.
Key Takeaways
- Gemini 3 Pro generates working boilerplate code for common ML patterns
- Always review generated code for data pipeline correctness and memory handling
- Combine multiple prompts to scaffold entire training pipelines
- Specify framework, dataset format, and hardware context for best results
- Generated evaluation code should always be verified against known baselines
Why ML Engineers Use Gemini 3 Pro for Code Generation
The ML development workflow has predictable patterns that are well-understood but time-consuming to implement correctly. A training loop is a training loop. Data augmentation strategies follow consistent logic. Callbacks for checkpointing, early stopping, and learning rate scheduling exist in standard forms.
Gemini 3 Pro draws on training data that includes these patterns. The model generates functional implementations faster than most engineers can write them from scratch, and the generated code tends to follow current best practices from established open-source repositories.
The use case is scaffolding, not autonomous coding. You review, verify, and customize what Gemini 3 Pro generates. But the review and customization is faster than building from scratch, and the generated code often handles edge cases that engineers forget when writing boilerplate from memory.
10 Best Gemini 3 Pro Machine Learning Model Training Prompts
Prompt 1: PyTorch Data Pipeline with Augmentation
Write a PyTorch DataLoader implementation for [dataset type: image classification/object detection/segmentation/text classification] with the following specifications:
- Dataset location: [path or describe data structure]
- Data format: [describe file structure, e.g., train/val/test folders with class subfolders, CSV with image paths, etc.]
- Augmentation strategy for training: [basic (random flip, crop)/standard (flip, color jitter, resize)/heavy (mixup, cutout, advanced geometric transforms)]
- Augmentation for validation: [none/resize and normalize only]
- Image size: [e.g., 224x224]
- Normalization: [ImageNet standards / dataset-specific statistics]
- Batch size: [number]
- Number of workers: [number, typically 4-8]
- Pin memory: [True/False depending on GPU availability]
Include:
- Dataset class with __len__ and __getitem__
- DataLoader creation for train/val/test splits
- Visualization function to check augmented samples
- Estimated memory requirements for a batch
Framework: PyTorch. Use torchvision transforms where appropriate.Why this prompt structure works: Data pipelines are the most common source of bugs in ML projects. This prompt forces specification of all the parameters that affect data loading behavior, reducing the chance of mismatches between training and inference.
Prompt 2: Training Loop with Logging and Callbacks
Write a complete PyTorch training loop for a [task type: image classifier/object detector/segmentation model/language model] using the following setup:
- Model: [model architecture, e.g., ResNet50, YOLOv8, U-Net, BERT]
- Optimizer: [optimizer type, e.g., AdamW, SGD with momentum]
- Learning rate: [number], with [learning rate scheduler type: cosine annealing/step decay/reduce on plateau]
- Loss function: [loss type appropriate to task]
- Number of epochs: [number]
- Hardware: [single GPU/multi-GPU/CPU]
- Gradient clipping: [value or none]
- Mixed precision training: [True/False]
Include:
- Full training loop with epoch-level and batch-level logging
- Learning rate scheduler step calls at correct intervals
- Gradient accumulation for large batch sizes
- Best model checkpoint saving based on validation metric
- Early stopping if validation metric plateaus
- Training time per epoch estimation
- Memory usage logging
Output a complete, runnable training script.Why this prompt structure works: Training loops are repetitive but critical. This prompt generates complete implementations that include the logging, checkpointing, and error handling that engineers often skip when writing loops from scratch.
Prompt 3: TensorFlow/Keras Model Training Script
Write a complete TensorFlow/Keras training script for [task type] with the following specifications:
- Model: [pre-trained model name from tf.keras.applications or custom model]
- Input shape: [e.g., (224, 224, 3)]
- Number of classes: [number]
- Optimizer: [optimizer with any specific configuration]
- Loss function: [sparse_categorical_crossentropy/categorical_crossentropy/binary_crossentropy/custom]
- Metrics: [list of metrics to track]
- Callbacks to include: [ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard, CSVLogger]
- Epochs: [number]
- Batch size: [number]
- Validation split: [percentage if no explicit validation set]
Include:
- Model compilation with exact optimizer and loss configuration
- Callback configuration with correct file paths and monitoring metrics
- Training with history return for later analysis
- Final model saving in recommended format (.keras or SavedModel)
- Memory optimization with mixed precision if applicable
Output a complete, runnable training script.Why this prompt structure works: TensorFlow/Keras has accumulated multiple API versions and best practices that change between releases. This prompt ensures you get code that matches current TF2.x conventions rather than legacy approaches.
Prompt 4: Class Imbalance Handling
Write PyTorch code to handle class imbalance for [task type: classification/detection] with the following dataset characteristics:
- Total samples: [number]
- Class distribution: [describe imbalance, e.g., "90% class A, 7% class B, 3% class C" or provide class counts]
- Imbalance ratio: [e.g., 30:1 between majority and minority classes]
Generate three approaches and explain when each is most appropriate:
1. Weighted loss function implementation with correct class weight calculation
2. Oversampling strategy with a DataLoader that handles oversampling without data leakage
3. Combined weighted loss + oversampling approach
Include:
- Class weight calculation from dataset
- WeightedRandomSampler implementation
- Loss function modification for weighted objectives
- Verification that sampler produces balanced batches
- Expected training behavior differences between approaches
Framework: PyTorch.Why this prompt structure works: Class imbalance is a common problem that requires different strategies depending on the severity and type of imbalance. This prompt generates all three major approaches and the verification code to check that implementation is correct.
Prompt 5: Cross-Validation Training Setup
Write a k-fold cross-validation training setup in [PyTorch/TensorFlow] for [task type] with the following specifications:
- Number of folds: [number, typically 5 or 10]
- Dataset: [describe dataset size and structure]
- Model: [model architecture]
- Training configuration: [optimizer, learning rate, epochs]
- Random seed: [number for reproducibility]
Include:
- KFold split generation with stratified sampling for classification tasks
- Per-fold model initialization
- Per-fold training with separate validation for each fold
- Aggregation of fold-level metrics (mean and standard deviation)
- Best model selection across folds
- Final model training on full dataset using best fold hyperparameters
- Cross-validation results summary with per-fold and aggregate metrics
Framework: [PyTorch/TensorFlow]. Use sklearn.model_selection.KFold.Why this prompt structure works: Cross-validation is essential for reliable model evaluation but the setup code is repetitive. This prompt generates correct stratified splits and proper metric aggregation that many engineers get wrong when writing from memory.
Prompt 6: Model Evaluation and Metrics Computation
Write comprehensive model evaluation code for [task type] that computes the following metrics:
Task type: [classification/detection/segmentation/regression]
- Dataset: [describe validation/test set]
- Model: [model to evaluate]
- Threshold (if applicable): [decision threshold]
Required metrics:
- [List all metrics, e.g., accuracy, precision, recall, F1, AUC-ROC, AP for detection, IoU for segmentation]
Include:
- Inference function that handles batch processing and device placement
- Metric computation with clear separation between metric definition and computation
- Per-class metrics for multi-class problems
- Confusion matrix generation and visualization code
- ROC and Precision-Recall curve generation
- Threshold optimization based on metric of interest
- Results summary table
Framework: [PyTorch/TensorFlow]. Use sklearn.metrics where appropriate.Why this prompt structure works: Evaluation code is often written hastily after training, leading to inconsistent metrics between experiments. This prompt generates complete, correct evaluation code that you run before you have any bias toward positive results.
Prompt 7: Transfer Learning Setup
Write a transfer learning setup in [PyTorch/TensorFlow] for [task type] with the following specifications:
- Source task: [what the pre-trained model was trained on, e.g., ImageNet classification, COCO detection]
- Target task: [what you are adapting to]
- Pre-trained model: [exact model name]
- Freeze strategy: [full freeze / partial freeze (which layers) / fine-tune all]
Include:
- Pre-trained model loading with correct weight handling
- Architecture modification for new task (e.g., changing classifier head)
- Freeze/unfreeze logic with clear layer naming
- Learning rate configuration for different layer groups (lower LR for frozen layers)
- Progressive unfreezing schedule if applicable
- Validation that model loads correctly before training
- Feature extraction mode vs. fine-tuning mode switching
Framework: [PyTorch/TensorFlow].Why this prompt structure works: Transfer learning setups are straightforward in concept but the implementation details (correct layer names, proper LR configuration, weight initialization) trip up many engineers. This prompt generates correct implementations that follow established practices.
Prompt 8: Experiment Tracking Integration
Write a training script that integrates with [MLflow/Weights & Biases/Neptune/TensorBoard] for experiment tracking with the following specifications:
- Tracking URI/logging directory: [path]
- Experiment name: [name]
- Run name format: [how to name individual runs]
Include logging of:
- Hyperparameters (model config, training config, data config)
- Training metrics per step/epoch (loss, learning rate, any custom metrics)
- Validation metrics per epoch
- System metrics (GPU utilization, memory usage if available)
- Artifacts (best model checkpoint, final model, any visualizations)
- Notes/tags for run identification
Show integration within a standard [PyTorch/TensorFlow] training loop with minimal overhead.Why this prompt structure works: Experiment tracking is essential for reproducible ML but the integration code is boilerplate that engineers often skip because it takes time to set up correctly. This prompt generates drop-in integration that adds minimal overhead to existing training loops.
Prompt 9: Distributed Training Configuration
Write a distributed training setup in [PyTorch/TensorFlow] for the following scenario:
- Hardware: [single node multiple GPUs/multi-node GPUs]
- Number of GPUs: [number]
- Communication backend: [NCCL for GPU/Gloo for CPU]
- Batch size: [per-GPU batch size]
- Learning rate scaling: [linear scaling rule / other]
Include:
- Distributed sampler configuration for DataLoader
- Multi-GPU training loop with correct loss scaling
- Gradient synchronization across GPUs
- Evaluation on single GPU or multiple GPUs (specify which)
- Mixed precision training configuration for distributed setup
- Checkpoint saving that works across distributed runs
- Launch command for [torchrun/tf.distribute.MultiWorkerMirroredStrategy]
Framework: [PyTorch/TensorFlow].Why this prompt structure works: Distributed training bugs are notoriously difficult to debug because they manifest as subtle accuracy degradation rather than crashes. This prompt generates correct implementations that avoid the common pitfalls of gradient synchronization, batch size scaling, and checkpoint management.
Prompt 10: Hyperparameter Tuning with Optuna
Write a hyperparameter tuning setup using Optuna for [task type] with the following search space:
- Model: [base model architecture]
- Search space: [define parameter ranges, e.g., learning_rate: 1e-5 to 1e-3, batch_size: 16/32/64, weight_decay: 0 to 0.1]
- Optimization objective: [metric to optimize, e.g., validation_f1, validation_loss]
- Number of trials: [number]
- Pruning strategy: [none/median_pruner/median pruner with warmup]
Include:
- Objective function that trains model and returns metric to optimize
- Study creation with correct direction (minimize/maximize)
- Pruner configuration
- Storage configuration for study results (SQLite/remote database)
- Callback to log best trial during search
- Final training with best hyperparameters on full training set
- Comparison of best vs. baseline or default hyperparameters
Framework: [PyTorch/TensorFlow] with Optuna integration.Why this prompt structure works: Hyperparameter tuning is computationally expensive, so efficient implementation matters. This prompt generates Optuna setups that correctly structure the objective function, handle failures gracefully, and produce comparison results.
How to Get Better Results from ML Code Prompts
Specify Framework and Version
Always state PyTorch or TensorFlow explicitly, along with any version constraints. Generated code for the wrong framework is useless, and version mismatches can introduce subtle bugs.
Describe Data Format Precisely
Data pipeline bugs are the most common cause of ML failures. Describe your data format precisely: folder structure, file naming conventions, label formats, and any quirks in the data that might require special handling.
Include Hardware Context
GPU memory constraints, mixed precision training, and distributed training requirements all affect what code is appropriate. Specify your hardware setup to get implementations that actually run on your system.
Request Verification Functions
Generated code should include data pipeline verification (checking that augmentation produces expected outputs), memory usage checks, and metric baseline verification. Request these explicitly.
FAQ
Can Gemini 3 Pro generate production-ready ML code?
Gemini 3 Pro generates functional boilerplate code that follows current best practices. It is not a replacement for understanding your data and problem, but it significantly accelerates the scaffolding phase. Always review generated code for correctness before using it in production.
How do I handle generated code that doesn’t match my framework version?
Generated code is based on training data that may not reflect the latest framework versions. If you encounter API mismatches, specify your framework version explicitly in the prompt and ask for alternatives for deprecated functions.
What should I do when generated training code produces unexpected loss behavior?
Loss anomalies almost always trace back to data pipeline issues (labels not matching inputs, normalization applied incorrectly, augmentation breaking label consistency) rather than model issues. Review your data pipeline first before modifying the model architecture.
Can I combine multiple prompts into a single training pipeline?
Yes. Use the data pipeline prompt first to scaffold data loading, the training loop prompt to scaffold training, the evaluation prompt for metrics, and the experiment tracking prompt for logging. Combine them into a single script with consistent configuration management.
Conclusion
Machine learning development has a scaffolding problem. The interesting work (model architecture, feature engineering, problem framing) cannot start until the boilerplate (data loading, training loops, evaluation, checkpointing) is correct, and the boilerplate is time-consuming to write without adding bugs.
Gemini 3 Pro solves the scaffolding problem by generating production-quality implementations of the repetitive patterns that make up 70-80% of ML code. The 10 prompts in this guide cover the full development workflow from data loading to hyperparameter tuning.
Use these prompts to accelerate the boring parts of ML development. But remember: the AI generates the scaffolding. You still have to understand what it built and verify that it matches your specific problem.