Feature Engineering Idea AI Prompts for Machine Learning Engineers

Quick Answer

We identify that feature engineering is the critical differentiator in ML model performance, often more impactful than the algorithm itself. To overcome creative bottlenecks and the curse of dimensionality, we utilize AI prompts to generate high-quality, context-aware feature ideas. This guide provides a structured framework for prompting AI to transform raw data into predictive signals.

Key Specifications

The Art and Science of Feature Engineering

Ever wonder why two machine learning teams can use the exact same algorithm and dataset, yet one achieves 95% accuracy while the other struggles to break 70%? The secret isn’t in the model architecture—it’s in the feature engineering. This is the stage where raw data is sculpted into predictive signals, and it remains the most impactful, yet often most challenging, part of the entire ML pipeline. It’s the ultimate embodiment of the “garbage in, garbage out” principle; even the most sophisticated deep learning model will fail if it’s fed uninformative or noisy data. This creative bottleneck is where projects stall and potential is lost.

So, what exactly is feature engineering? In simple terms, it’s the art of transforming raw, messy data into a structured format that a machine learning model can actually understand and learn from. It involves everything from extracting the day of the week from a timestamp to creating complex interaction terms. However, this process introduces a critical challenge: the curse of dimensionality. Every new feature you add increases the complexity of your model’s search space, making it harder to find a generalizable pattern and increasing the risk of overfitting. This is why thoughtful feature selection is just as important as feature creation.

This is where your AI co-pilot enters the workflow. Overcoming “feature blindness”—that feeling of being stuck staring at the same columns in a CSV file—is a common struggle for even seasoned data scientists. Instead of relying solely on manual brainstorming, you can use Large Language Models (LLMs) as a creative partner to generate a high volume of testable feature ideas. By using specific, context-rich prompts, you can move beyond simple statistical features and explore creative transformations that you might have otherwise missed, dramatically accelerating your path to a more powerful model.

The Prompting Framework: How to Talk to an AI About Your Data

An AI model, no matter how advanced, is not a mind reader. It doesn’t know your business context, the nuances of your data, or the specific objective of your model. Simply asking “give me feature ideas” is like walking into a library and asking for “a good book”—you’ll get a generic response that’s unlikely to be useful. The quality of the features you get back is directly proportional to the quality of the information you put in. To get truly insightful, actionable suggestions, you need to provide a structured, context-rich prompt. This is the difference between a vague brainstorming session and a focused, expert-level consultation.

The Anatomy of a Powerful Feature Prompt

A well-structured prompt acts as a project brief for your AI co-pilot. It eliminates ambiguity and guides the model toward the exact type of thinking you need. Based on my experience building models for everything from e-commerce churn to real-time fraud detection, I’ve found that a robust prompt always contains three essential components. Getting these right will save you hours of back-and-forth and yield far more creative and effective ideas.

Here are the non-negotiable components of a powerful feature engineering prompt:

• Context (The “Why”): This is where you set the stage. You must tell the AI the domain you’re working in (e.g., “financial services,” “e-commerce,” “healthcare”) and the specific goal of your machine learning model. Are you trying to predict customer churn, classify fraudulent transactions, or forecast inventory demand? This context is critical because it allows the AI to generate features that are logically relevant to the problem. For instance, for a churn model, features related to customer support ticket history are highly relevant, whereas for a fraud model, they are not.
• Data Schema (The “What”): Next, you must give the AI a clear picture of the raw materials it has to work with. Provide a list of your column names and their corresponding data types. Don’t just list them; briefly describe what they represent if the name isn’t self-explanatory. This prevents the AI from hallucinating columns that don’t exist or misinterpreting what a column means.
• Desired Output (The “How”): Finally, tell the AI exactly how you want the results formatted. A structured format makes it easy to review, compare, and select ideas. I almost always ask for a table with specific columns, such as “Feature Name,” “Description,” “Potential Value,” and “Engineering Complexity.” This forces the AI to be concise and organized, giving you a clean, actionable list instead of a wall of text.

Here is a template I use regularly:

Role: You are a senior data scientist specializing in [Domain, e.g., retail e-commerce]. Goal: My objective is to build a model that [Model Goal, e.g., predicts the likelihood of a user making a repeat purchase within 30 days]. Data: Here is a sample of my data schema:

• user_id (string, unique identifier)
• first_purchase_date (datetime)
• last_purchase_date (datetime)
• total_lifetime_value (float)
• items_purchased (integer, JSON array of item IDs)
• marketing_channel (string, e.g., ‘email’, ‘social’, ‘organic’) Task: Generate 10 new feature ideas. Please present them in a markdown table with the following columns: “Feature Name,” “Description,” “Rationale,” and “Engineering Complexity (Low/Medium/High).”

Iterative Brainstorming and Refinement

Your first prompt is a starting point, not the finish line. The real magic happens when you engage in a conversational loop, treating the AI like a junior analyst you can delegate tasks to. This iterative process allows you to refine, narrow, and expand upon the initial suggestions. It’s a powerful way to explore the feature space methodically without getting overwhelmed.

Let’s say the AI gave you a list of 15 great ideas, but some are too complex to implement right now. You can simply follow up with a refinement prompt:

“Thanks. From that list, I want to focus on low-complexity features first. Please filter your previous suggestions and provide only the ones you rated as ‘Low’ complexity. For each, add a one-line Python pseudocode example for how to calculate it.”

This approach allows you to drill down into specific areas. You might ask for only interaction features, only time-based features, or only features that can be created using SQL window functions. You can also ask the AI to combine or simplify its own suggestions. For example: “The ‘Average Items Per Purchase’ feature is good, but can you suggest a more robust version that accounts for users with only one purchase?” This pushes the AI to think more deeply about edge cases, a hallmark of a good data scientist.

Golden Nugget: A common pitfall is to accept the first set of suggestions without scrutiny. Always ask the AI to justify its choices. The “Rationale” column in the output format is your best friend. If an AI can’t provide a logical reason for a feature’s inclusion, it’s probably not a good fit.

Setting Constraints and Guardrails

Just as important as telling an AI what to do is telling it what not to do. Without clear constraints, an LLM might suggest features that are theoretically interesting but practically useless or, even worse, dangerous to your model’s integrity. Setting guardrails is a critical step for ensuring the suggestions are not only creative but also responsible and production-ready.

Data leakage is the most insidious problem in machine learning. A feature that inadvertently includes information from the future (i.e., information that wouldn’t be available at the time of prediction) will give you a model that looks amazing in testing but fails spectacularly in the real world. In my work, I’ve learned to be explicit about this. For example, if you’re predicting churn based on user activity in the first week, you must forbid the AI from using any data from week two.

Here are the guardrails I always include in my prompts:

• Prohibit Data Leakage: Explicitly state that features must not use information that would not be available at the time of prediction. For a churn model, you might add: “Do not suggest any features that use data from after the user’s potential churn date.”
• Limit Complexity: Be realistic about your engineering capacity. If you’re working with a small team or limited compute, you can’t afford to engineer thousands of complex features. You can constrain the AI by saying: “Prioritize features that can be generated with simple SQL queries or Pandas transformations. Avoid features requiring external API calls or complex NLP models.”
• Ensure Availability at Inference: This is a related but distinct constraint. A feature might be available during training but not during inference. For example, a feature like “average purchase value of all users in the last 24 hours” requires a full table scan that might be too slow for a real-time prediction API. You can prompt: “Suggest features that can be computed efficiently for a single user at inference time, without requiring large-scale aggregations.”

By setting these guardrails, you shift the AI’s role from a purely creative engine to a pragmatic and context-aware partner. This ensures the feature ideas it generates are not just a brainstorming exercise but a concrete, actionable list that you can start engineering with confidence.

Foundational Features: Establishing a Strong Baseline

What separates a mediocre model from a production-ready one? Often, it’s not a complex algorithm but the quality of its foundational features. Before you chase interaction terms or deep learning embeddings, you need to ensure your raw data is prepared correctly. This is where you establish a strong, reliable baseline. Getting this right is non-negotiable; it’s the bedrock upon which all other feature engineering efforts are built.

Think of this stage as prepping your ingredients before you start cooking. You wouldn’t put a whole, unwashed potato into a stew. Similarly, you can’t just feed a skewed, unscaled column of data into your model and expect optimal results. Your AI co-pilot can act as a meticulous sous-chef, helping you identify which ingredients need chopping, which need seasoning, and how to prepare them for the final dish.

Statistical and Numerical Transformations: The Data Scientist’s Toolkit

The bread-and-butter of feature engineering lies in mastering basic statistical transformations. These are the techniques you’ll apply in almost every project. The key is knowing when to apply them, and your AI prompts should be designed to uncover these opportunities.

Consider a feature like transaction_amount. In a typical e-commerce dataset, this will be heavily right-skewed—a few large transactions can dramatically pull the mean, making it less representative of the typical transaction. A model trained on this raw data might overemphasize outliers. This is where you prompt your AI to diagnose the problem and suggest solutions.

Here are some example prompts to guide your AI:

• “I have a numerical column named user_page_load_time with a mean of 2.5 seconds but a standard deviation of 15 seconds, indicating significant outliers. Suggest three feature engineering transformations to make this data more suitable for a linear model, and explain the benefit of each.”
• “Analyze the following column summary for customer_lifetime_value: [paste summary stats, skewness, kurtosis]. Given its high positive skew, which is better for model performance: a log1p transformation or a square root transformation? Provide the Python code for both and explain the trade-offs.”
• “My dataset contains several numerical features with vastly different scales, such as age (18-80) and annual_income (30,000-250,000). Generate a plan for applying scaling. Should I use StandardScaler or MinMaxScaler for a Random Forest model, and why?”

Golden Nugget: A common mistake is applying scaling before splitting your data into training and test sets. This causes data leakage, where information from the test set “leaks” into the training process, giving you deceptively high performance metrics that won’t hold up in production. Always fit your scaler on the training data only, then use that fitted scaler to transform both your training and test sets.

Categorical Encoding Strategies: Translating Words into Numbers

Your model doesn’t understand “red,” “blue,” or “green.” It only understands numbers. The way you convert these categories into a numerical format can have a profound impact on your model’s performance, especially with high-cardinality features (columns with many unique values). While one-hot encoding is the default, it’s often not the best choice.

Your AI can help you navigate the trade-offs between different encoding strategies. A simple one-hot approach for a zip_code column with 2,000 unique values would create 2,000 new columns, a classic recipe for the curse of dimensionality. A more sophisticated approach is needed.

Use prompts like these to explore better options:

• “I’m working on a classification problem to predict customer churn. The dataset has a city column with 500 unique values. Compare one-hot encoding, target encoding, and frequency encoding for this feature. What are the pros and cons of each in the context of preventing overfitting and model interpretability?”
• “For a tree-based model like XGBoost, is it better to use one-hot encoding or ordinal encoding for an education_level column (‘High School’, ‘Bachelor’s’, ‘Master’s’, ‘PhD’)? Explain how tree models split on these different encodings.”
• “Generate a Python code snippet using category_encoders library to apply target encoding to a product_id feature. Include a comment explaining how to prevent target leakage by using a cross-validation scheme within the encoding process.”

Choosing the right encoding strategy is a trade-off between model complexity, interpretability, and performance. Your AI can help you articulate these trade-offs, allowing you to make an informed decision rather than just following a default workflow.

Handling Time and Date Information: Unlocking Cyclical and Event-Based Insights

A raw timestamp like 2025-10-26 14:30:00 is a goldmine of information, but it’s useless to a model in its native format. The real value comes from engineering features that capture the underlying patterns of time. Is this a peak activity hour? Is it a weekend? How much time has passed since a key event?

Extracting these features manually is tedious and error-prone. This is a perfect task for an AI partner. You can give it a high-level goal and get back a comprehensive list of actionable ideas.

For example, you could provide this prompt:

“Given a transaction_timestamp column in my e-commerce dataset, suggest 10 cyclical and event-based features I can engineer. Include features like ‘hour of day,’ ‘day of week,’ ‘is_weekend,’ and ‘time_since_last_event’ for a specific user. For cyclical features like ‘hour’ or ‘month,’ explain how to encode them using sine/cosine transformations to preserve their cyclical nature.”

The AI’s response would not only give you the feature ideas but also the critical context about why a simple integer for “hour of day” is problematic (because hour 23 is as close to hour 0 as hour 1 is, but 23 is far from 0 numerically). This is the kind of expert insight that elevates a baseline model into a sophisticated one. By systematically transforming these foundational elements, you build a robust and reliable starting point for any machine learning project.

Advanced Feature Creation: Interaction, Aggregation, and Domain-Specific Ideas

You’ve cleaned your data and encoded your categorical variables. Your model is running, but the accuracy has plateaued. This is the point where most machine learning engineers get stuck, believing they’ve hit the ceiling of what their data can offer. In reality, the raw data is just the starting point. The real predictive power is unlocked through advanced feature engineering—the art of crafting new signals that reveal hidden relationships.

This is where AI prompting becomes a true force multiplier. Instead of manually brainstorming combinations or writing complex SQL window functions from scratch, you can use AI as a creative partner to surface non-obvious patterns and domain-specific features you might have missed. It’s about transforming your raw data into a rich, predictive dataset that gives your model a significant edge.

Creating Interaction and Polynomial Features

Linear models assume a straight-line relationship between features and the target. But what if the impact of one feature depends on the value of another? This is where interaction features come in. By combining features (e.g., multiplying them), you can help the model capture these synergistic, non-linear effects. For instance, in a real estate model, square_footage alone is useful, but square_footage * number_of_bedrooms might be a powerful signal for family-sized homes.

Here’s a prompt template to generate these ideas:

Prompt Template: “I’m building a regression model to predict [target variable, e.g., customer churn]. My current features include [list key numerical and categorical features, e.g., ‘monthly_charges’, ‘tenure_months’, ‘contract_type’, ‘total_data_usage’].

Act as an expert data scientist. Suggest 5-7 interaction or polynomial features that could capture non-linear relationships. For each suggestion, explain the potential business logic behind it. Also, include a cautionary note for each feature about the risk of overfitting and how to validate it.”

The AI might suggest features like monthly_charges * tenure_months to capture how the impact of price on churn changes for long-term customers, or a polynomial feature like tenure_months^2 to model a non-linear decay in churn probability. A key insight from experience is to always start with interaction features that have a clear business hypothesis before blindly adding polynomial terms. The latter can rapidly increase model complexity and overfitting risk. A good practice is to use regularization techniques like Lasso or Ridge to automatically penalize less important complex features.

Golden Nugget: A common mistake is creating interaction features with high multicollinearity (e.g., age and age_squared are highly correlated). Before adding them, check the Variance Inflation Factor (VIF). A VIF greater than 5 or 10 is a red flag that your model’s coefficients will be unstable. Your prompt can specifically ask the AI to suggest features that “minimize multicollinearity with existing features.”

Rolling Window and Time-Series Aggregations

For any data with a temporal component—user activity, stock prices, sensor readings—a single point-in-time measurement is often misleading. You need context. What was the user’s behavior recently? How does today’s value compare to the last week’s average? This is where rolling window aggregations are indispensable.

Sequential data is full of patterns that are only visible when you look at trends over time. A user who suddenly makes 10 purchases in one day is very different from a user who makes 10 purchases over 90 days, even if their total purchase count is the same. Your model needs to see this velocity.

Use this prompt to generate time-series features:

Prompt Template: “I’m working with a dataset of user transactions for an e-commerce platform. The key columns are user_id, transaction_timestamp, and purchase_amount.

Generate a list of time-series aggregation features to help predict the ‘next_purchase_amount’. Suggest features using rolling windows of 7, 30, and 90 days. Include ideas for:

1. Simple moving averages (SMA).
2. Exponential moving averages (EMA) to give more weight to recent activity.
3. Cumulative sums or counts (e.g., total purchases to date).
4. Volatility measures (e.g., standard deviation of purchase amounts over the window).

For each feature, explain why it would be valuable for this specific prediction task.”

The AI will likely suggest features like user_avg_spend_last_7d, user_purchase_count_last_30d, and user_spend_std_dev_90d. An expert tip is to also create “lag features” (e.g., the value from 7 days ago) and “delta features” (e.g., the difference between the 7-day rolling average and the 30-day rolling average). These features explicitly tell the model about recent trends and momentum, which are often powerful predictors.

Prompting for Domain-Specific Features

This is where AI truly shines, acting as a domain expert on demand. Generic feature engineering can only take you so far. A model predicting stock market movements needs features like “volatility,” while a model for customer retention needs “days since last purchase.” By asking the AI to adopt a specific persona, you can tap into a deep well of industry knowledge.

Use this persona-based prompting strategy to unlock domain-specific gold:

Prompt Template: “Act as a Senior Data Scientist with 10 years of experience in the [Domain, e.g., ‘FinTech’, ‘E-commerce’, ‘SaaS’] industry. I am building a model to predict [Specific Goal, e.g., ‘customer lifetime value’, ‘loan default’, ‘document sentiment’].

Based on your domain expertise, what are the top 5 most impactful, non-obvious features I should engineer from raw data like [mention raw data available, e.g., ‘transaction logs’, ‘user clickstream’, ‘financial statements’]?

For each feature, provide:

1. The feature name (e.g., ‘Recency-Frequency-Monetary Score’).
2. A brief definition.
3. The SQL or Python pseudocode for how to calculate it.”

Example AI Responses by Domain:

• E-commerce: The AI would likely suggest RFM Scores (Recency, Frequency, Monetary), Days Since Last Purchase, and Average Time Between Purchases. These are classic, high-impact features for segmentation and CLV prediction.
• Finance: For a stock prediction model, it might suggest Moving Average Convergence Divergence (MACD), Bollinger Bands, or Average True Range (ATR). These are standard technical indicators that capture momentum and volatility.
• NLP: When analyzing text, it could suggest Sentiment Score, Entity Count (number of people, organizations mentioned), or Readability Score (e.g., Flesch-Kincaid). These features convert unstructured text into quantifiable signals about tone, focus, and complexity.

By using these targeted, persona-driven prompts, you move beyond generic feature lists and start building a model that is deeply informed by the specific context of your problem. This is the difference between a model that works and a model that wins.

Feature Evaluation and Selection: From Ideas to Implementation

You’ve just generated a list of 20 brilliant feature ideas with your AI assistant. It’s an exciting moment, but it’s also where many machine learning projects stall. How do you separate the signal from the noise? A feature that feels impactful can be useless or, worse, harmful to your model. This is the critical bridge between creative brainstorming and rigorous, data-driven implementation.

Moving from raw ideas to a validated feature set requires a systematic approach. Instead of just building every feature and hoping for the best, you can use AI prompts to establish a hypothesis for each feature, select the most promising candidates, and even stress-test them for hidden flaws. This disciplined process turns a list of possibilities into a robust, high-performing model.

Generating Hypotheses and Success Metrics

Before you write a single line of feature engineering code, you must be able to answer why this feature should exist. This is where hypothesis generation comes in. For every potential feature, formulate a clear, testable statement. This practice forces you to think critically about the feature’s expected impact and provides a clear benchmark for its success.

Your AI Prompting Strategy: Use a structured prompt to formalize this process. For example, you can ask your AI assistant:

“For the proposed feature ‘time_since_last_purchase’ in a customer churn prediction model, generate a formal hypothesis statement. Also, suggest 2-3 appropriate metrics to evaluate its impact, considering both a baseline logistic regression model and a more complex gradient boosting model.”

The AI’s Hypothesis Output:

Hypothesis: Adding the ‘time_since_last_purchase’ feature will improve the model’s ability to distinguish between at-risk and loyal customers. We hypothesize that a shorter time since the last purchase correlates with a lower probability of churn, as it indicates recent engagement. This feature will provide a nuanced view of customer activity that a simple ‘total_purchase_count’ feature misses.

Success Metrics:

1. Primary Metric: Improvement in AUC-ROC score on a hold-out test set. A minimum increase of 0.02 will be considered a significant success.
2. Secondary Metric: Analysis of feature importance from the tree-based model. We expect ‘time_since_last_purchase’ to rank within the top 10 features.
3. Business Metric: If deployed, we would track the precision and recall of the model on a targeted “save” campaign for customers flagged as high-churn.

This output gives you a clear, measurable goal. You’re no longer just “trying a new feature”; you’re testing a specific, falsifiable claim about your data.

Prompting for Feature Selection Techniques

Once you have a set of features with clear hypotheses, you need to narrow them down. Throwing everything at your model can lead to overfitting, longer training times, and a less interpretable system. AI can be an excellent partner for generating the code and strategies needed for effective feature selection.

Here are a few ways to prompt your AI for this task:

• For Correlation Analysis: “Generate Python code using pandas and seaborn to create a correlation heatmap for my feature set. Include a threshold parameter (e.g., 0.85) to highlight features that are highly correlated with each other, suggesting potential multicollinearity issues.”
• For Feature Importance: “Write a Python script that trains a Random Forest Classifier on my dataset and then visualizes the feature importances as a horizontal bar chart. The script should use scikit-learn and matplotlib.”
• For Recursive Feature Elimination (RFE): “I want to use Recursive Feature Elimination to find the top 15 features for my regression problem. Provide a code snippet using scikit-learn’s RFE with a LinearRegression estimator. Explain how to interpret the ranking_ attribute.”

Golden Nugget: A common mistake is relying solely on a single feature selection method. My experience has shown that using a combination of techniques yields the most robust results. For instance, use a tree-based model’s feature importance for a first pass, then apply RFE on that reduced set. This hybrid approach is faster and often uncovers different types of valuable features that a single method might discard.

The “Bad Feature” Detector

This is perhaps the most underrated step in the feature engineering lifecycle. Before you commit to a feature, you need a ruthless critic to find its hidden flaws. This is where you can use AI to act as a “bad feature detector,” proactively identifying issues that could sabotage your model.

Your AI Prompting Strategy: Present your proposed feature list to the AI and ask it to play the role of a senior ML engineer reviewing your work.

“Act as a senior machine learning engineer. I’m building a model to predict customer lifetime value (CLV) and have proposed the following features:

• total_spend
• average_order_value
• days_since_first_purchase
• customer_city
• last_product_viewed_id

Analyze this list for potential issues like multicollinearity, high cardinality, data leakage, or proxy variables. For each issue you find, explain the risk and suggest a potential solution.”

The AI’s Critical Review:

1. Multicollinearity Risk:

• Features: total_spend and average_order_value.
• Risk: These two features are likely highly correlated. average_order_value is derived from total_spend (and order_count). This can destabilize linear models and reduce the interpretability of feature coefficients.
• Suggestion: Consider dropping one or creating a more nuanced feature like spend_volatility (standard deviation of order values).

2. High Cardinality:

• Feature: last_product_viewed_id.
• Risk: This categorical feature likely has thousands of unique values. One-hot encoding it would create a massive, sparse feature set, potentially leading to the curse of dimensionality and overfitting.
• Suggestion: Use techniques like Target Encoding or group the IDs into higher-level categories (e.g., ‘Electronics’, ‘Apparel’, ‘Home Goods’).

3. Data Leakage / Proxy Variable:

• Feature: total_spend.
• Risk: This is a classic proxy for the target variable, CLV. If a customer has already spent a lot, their future CLV is already partially realized. Including this feature will give you an unrealistically high accuracy during training, but the model will fail in production on new customers.
• Suggestion: Remove total_spend. Instead, engineer features based on rate of spending, like spend_per_day_since_first_purchase.

Case Study: Brainstorming Features for a Customer Churn Prediction Model

You’re staring at a blank notebook, tasked with building a churn prediction model for your SaaS company. The raw data is there, but the real magic—the features that will actually teach your model what to look for—feels elusive. How do you translate rows of user activity into signals that predict departure? This is where a strategic partnership with an AI co-pilot transforms a daunting brainstorming session into a highly productive, creative process.

Let’s walk through a realistic scenario to see this in action.

The Scenario: Raw Data from a SaaS Platform

Imagine our AI has access to a simplified, anonymized data warehouse for a project management SaaS tool. We have four key tables:

• customers: customer_id, signup_date, plan_tier (Basic, Pro, Enterprise), company_size.
• subscriptions: subscription_id, customer_id, start_date, end_date, status (active, canceled), monthly_recurring_revenue (MRR).
• support_tickets: ticket_id, customer_id, created_at, category (Billing, Technical, Account), status (open, closed).
• app_usage_logs: log_id, customer_id, login_timestamp, projects_created, tasks_completed.

Our goal is to engineer features that will help a model distinguish between a happy, engaged user and one who is quietly disengaging and about to churn.

The Prompting Session in Action

We start with a broad prompt to get the creative juices flowing.

My Initial Prompt:

“I’m building a binary classification model to predict customer churn for a B2B SaaS company. Here are my raw data tables: [paste schema for customers, subscriptions, support_tickets, app_usage_logs]. Brainstorm 10 potential features for my model, focusing on customer behavior, engagement, and support interactions.”

AI’s First Batch of Ideas (The Raw Output):

“Certainly. Based on the provided schemas, here are some initial feature ideas:

1. customer_tenure_days: Days since signup_date.
2. days_since_last_login: Calculated from the most recent login_timestamp.
3. total_support_tickets: A simple count of all tickets for a customer.
4. is_on_enterprise_plan: A binary flag for ‘Enterprise’ tier.
5. avg_tasks_per_week: Total tasks_completed divided by tenure.
6. ticket_close_rate: Number of closed tickets divided by total tickets.
7. recent_ticket_count: Number of tickets created in the last 30 days.
8. monthly_recurring_revenue: The latest MRR value.
9. projects_created: Total number of projects created.
10. has_open_tickets: A binary flag if any tickets are currently ‘open’.”

This is a solid start, a good baseline. But a model trained only on these static, cumulative features would miss the critical story of a user’s journey. The real churn signals are in the changes in behavior. Now, we refine.

My Follow-up Prompt (Refining for Dynamics):

“Good start. Now, I want to capture change and velocity. Give me features that measure shifts in user behavior over time. For example, how has their activity changed in the last 30 days compared to their overall average? Also, suggest some features that capture the nature of their support interactions.”

AI’s Refined Output (The Expert Insight):

“Excellent point. Static features miss the ‘why’ behind churn. Here are dynamic and interaction-based features:

• login_velocity_change: (Logins in last 14 days) / (Logins in previous 14-day period). A value < 1 indicates declining engagement.
• task_completion_decay: (Tasks completed in last 30 days) / (Total tasks completed). This highlights a drop-off in core product value.
• support_ticket_urgency: A score based on category (e.g., ‘Technical’ = 2 points, ‘Billing’ = 1 point) and status (e.g., ‘open’ = 3 points, ‘closed’ = 1 point) for tickets in the last 60 days. A high score means recent, unresolved frustration.
• days_since_last_ticket: A low value here could signal ongoing issues.
• mrr_to_usage_ratio: MRR / (tasks_completed + projects_created). A high ratio might indicate they are paying a lot but getting little value.”

This is the turning point. We’ve moved from “what happened” to “how is their behavior evolving,” which is far more predictive.

The Final Feature Shortlist and Justification

After refining and curating the AI’s suggestions, we arrive at a powerful shortlist. These are the features I would implement, based on this session.

Feature Name	Justification (The “Why”)	How to Engineer It
login_velocity_change	This is a golden nugget. A user who logs in daily and suddenly stops is a classic churn risk. This feature quantifies that slowdown, turning a vague feeling into a hard number.	Calculate the number of logins in the last 14 days and divide it by the number of logins in the 14 days prior. A value significantly below 1.0 is a major red flag.
support_ticket_urgency_score	Not all support tickets are equal. A user with three open technical issues is far more likely to churn than one who closed a single billing question. This feature captures that nuance.	Create a weighted scoring system. For example: score = (count of 'Technical' tickets * 3) + (count of 'Billing' tickets * 2). Apply this only to tickets from the last 60 days to keep it relevant.
days_since_last_core_action	Login can be a vanity metric (someone might just log in to check a notification). A core action, like completing a task, is a much stronger signal of value being derived.	Find the most recent login_timestamp where tasks_completed > 0. The number of days since that event is your feature. A long gap is a powerful predictor of churn.
mrr_to_usage_ratio	This feature directly measures value-for-money. A customer paying $500/month but only completing 10 tasks is getting a poor return compared to a $50/month user completing 100 tasks.	latest_mrr / (total_projects + total_tasks). This helps the model understand the context of the customer’s spending relative to their engagement.
plan_downgrade_flag	A customer moving from ‘Pro’ to ‘Basic’ is a huge signal of dissatisfaction. It’s a direct action indicating they are devaluing the service, even if they haven’t canceled yet.	A simple binary flag: 1 if a customer’s latest subscription plan_tier is lower than their previous one, 0 otherwise.

By moving from a generic prompt to a series of targeted, iterative requests, we’ve co-created a feature set that tells a story. We’re not just asking the model “Is this user active?”; we’re asking “Is this user’s engagement decaying, are they frustrated with support, and do they feel they’re getting their money’s worth?” This is the difference between a model that guesses and a model that understands.

Conclusion: Augmenting Your Feature Engineering Workflow

So, where does this leave you? You’ve seen how a well-crafted prompt can act as a tireless brainstorming partner, systematically expanding your feature space in ways that manual ideation often misses. The key advantage isn’t just speed; it’s the ability to uncover non-obvious, high-signal features—like interaction terms or velocity-based aggregations—that can dramatically boost model performance. By structuring your requests, you transform a vague brainstorming session into a targeted, repeatable process for generating predictive power.

However, the most critical insight is this: AI is a powerful collaborator, not a replacement for your expertise. The AI will suggest features that are statistically interesting but computationally expensive, or ones that create data leakage. It doesn’t understand your business constraints or the nuances of your data pipeline. Your role is to be the final arbiter, judging the feasibility, complexity, and true business relevance of every suggestion. This is the “human-in-the-loop” that turns a good model into a great one.

Think of the AI as a junior data scientist who has read every textbook but has zero field experience. It provides the raw material; you provide the seasoned judgment.

Your next step is to put this into practice. Don’t let this knowledge remain theoretical. Pick one prompt template from this article and apply it to your current project right now. Whether you’re predicting churn, forecasting sales, or classifying images, take five minutes to run the prompt and see what novel features the AI suggests. The gap between reading about a technique and successfully applying it is bridged by action. Start building that bridge today.

Expert Insight

Frequently Asked Questions

Q: Why is feature engineering more important than the model choice

Because even the best algorithm cannot compensate for uninformative or noisy data; feature engineering transforms raw data into the predictive signals the model needs to learn

Q: What is ‘feature blindness’

It is the common struggle where data scientists get stuck looking at the same columns, unable to brainstorm new transformations; AI prompts help overcome this by acting as a creative partner

Q: How does AI help with the ‘curse of dimensionality’

While AI generates many ideas, a structured prompting framework helps you focus on high-impact, relevant features, allowing for better manual selection to avoid adding unnecessary noise