When starting a new ML project and needing to choose the right model family rather than defaulting to deep learning for everything.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Machine Learning & AI Engineering - Use case: Model Selection Advisor - Source task: - Recommend the right model for {{describe_the_problem_and_data}}. Dataset size: {{rows_features}}. Latency requirement: {{inference_time}}. Explainability requirement: {{explainability_requirement}}. Infrastructure: {{infrastructure}}. - Step 1: compare 3 candidate model families (e.g., tree-based, neural, linear) with pros and cons for this specific problem. - Step 2: recommend the best starting model with justification. - Step 3: recommend hyperparameters to tune first. - Step 4: describe the evaluation protocol (train/val/test split, cross-validation strategy, evaluation metrics). # Goal 3-model family comparison, recommendation with justification, hyperparameter tuning plan, and evaluation protocol. # Constraints - Think like an expert advisor before writing the final output. - Ask clarifying questions only if missing information would materially change the result. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output 3-model family comparison, recommendation with justification, hyperparameter tuning plan, and evaluation protocol.
{{double-curly}} with your real context.When starting a new ML project and needing to choose the right model family rather than defaulting to deep learning for everything.
Gradient boosted trees (XGBoost, LightGBM) outperform deep learning on tabular data in 80% of cases — start there for structured data.
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