Machine Learning Engineers use this to generate implementable features that respect production constraints (train/serve parity, monitoring, drift). Applied Data Scientists lean on it to turn raw fields into testable hypotheses, instead of another round of generic transforms. Analytics Engineers benefit when feature work requires clean joins, windowed aggregations, and consistent definitions across datasets. ML Consultants use it to propose a prioritized plan that clients can validate quickly, with leakage and proxy risks called out.

E-commerce and marketplaces get value from recency/frequency/monetary features, price sensitivity proxies, cohort behavior, and promotion-driven seasonality checks. SaaS companies use it for churn or expansion prediction, where good features often come from product telemetry windows, “time since key action,” and account-level rollups. Fintech and lending apply it to risk models that require careful leakage control, stability under drift, and features that remain valid at decision time. Healthcare and life sciences benefit when measurements are noisy and irregular, making aggregation logic, missingness indicators, and robust time-based validation essential.

A typical prompt like “Write me some feature engineering ideas for my dataset” fails because it: lacks the target definition and decision timing (so it suggests leakage-prone proxies), provides no structure for “what it is / why / how / what can go wrong / how to validate,” ignores the model family (leading to pointless interactions or redundant transforms), produces long unprioritized lists instead of a ranked plan, and misses production realities like train/serve skew and drift monitoring.

Yes. Customize it by adding your target definition (including the timestamp when the prediction is made), your available raw tables/fields with granularity, and your model family plus evaluation split. If you have constraints, include them plainly: latency budget, allowable data sources, and whether features must be explainable. A good follow-up is: “Given these columns and the prediction time, propose the top 10 features with Low leakage risk, and include an ablation plan plus monitoring signals for drift.” If the prompt asks clarifying questions, answer them before you request the final prioritized list.

The biggest mistake is leaving the prediction moment vague—bad: “Predict churn,” better: “Predict churn in the next 30 days using only data available as of day 7 after signup.” Another common error is not stating the model family; “any model” leads to mismatched suggestions, while “logistic regression” or “LightGBM” sharpens interactions and encodings. People also skip data granularity and keys—bad: “I have users and orders,” better: “users(user_id), orders(order_id,user_id,created_at,amount), events(user_id,event_time,event_name).” Finally, teams forget to mention deployment constraints; if serving cannot compute 30-day windows in real time, say so and request batch-friendly alternatives.

This prompt isn’t ideal for one-off toy projects where you won’t validate or deploy anything, because its value comes from careful testing and production-safe thinking. It’s also not a fit if you have not defined the target label and the prediction timing yet; you’ll get a lot of “maybe” ideas until that’s nailed down. And if your real bottleneck is bad labels, missing instrumentation, or data quality, you should prioritize data collection and auditing before heavy feature design. In those cases, start with a labeling review and instrumentation plan instead.