Prompt-Driven Feature Engineering Techniques

Feature engineering is where most of the lift in a machine learning project hides. AI is unusually good at suggesting features because it has seen thousands of problem types — but only if your prompt tells it what kind of data you have and what you are trying to predict. This topic gives you the patterns to generate feature ideas and feature code in one move.

1. Introduction

The fastest way to improve a struggling model is rarely a fancier algorithm — it is a sharper feature. Lag values, ratios, time-since-event, target-encoded categoricals, and aggregated session windows can push a baseline from "mediocre" to "shippable" in a single afternoon. AI accelerates this work enormously, because suggesting features is mostly pattern recognition across problem shapes. The catch is that feature engineering also has unusually high leakage risk: a careless target-encoded feature can inflate validation scores by 20% and then collapse in production. This tutorial shows you how to prompt for impact without leakage.

2. The Concept Explained

Useful features fall into a small number of families. Time features: extracted parts of a timestamp (hour, day-of-week), lags, rolling aggregates, and time-since-event. Ratio features: one column divided by another (price per unit, sessions per day). Aggregate features: groupby-then-merge values (customer's mean spend, region's median churn rate). Interaction features: combinations (plan_type × tenure_months). Target-aware features: target encoding, weight-of-evidence, leave-one-out means — powerful and dangerous. Text features: length, sentiment, keyword presence, embeddings.

A useful analogy: features are the sentences your model reads. Lag values say "what happened recently". Ratios say "how does this compare". Aggregates say "what is normal for this group". The job of feature engineering is to translate the raw timestamps and IDs of your dataset into sentences the model can understand.

3. The Problem Without This Technique

Give me feature engineering ideas for my model.

Act as a senior ML engineer designing features for an
imbalanced churn-prediction model.

Dataset: customers_df (~480k rows, observation_date per row).
Target: churn_within_90d (binary), prevalence ~7%.
Available columns:
  signup_date, plan_type (basic|pro|enterprise),
  monthly_revenue, last_login_date, support_tickets_30d,
  feature_usage_score (0-100), region, billing_country,
  upgrade_events (count).

Leakage rule: ANY feature must be computable as of
observation_date — do NOT use information from after it.

Task: propose 15 candidate features grouped by family
(time, ratio, aggregate, interaction, target-aware).
For each: name, one-line formula, expected predictive
signal, leakage risk (low/med/high).
Then write Pandas code that generates the top 8 features
into a new DataFrame `features_df` keyed on customer_id
and observation_date.

4. The Solution

The pattern is: problem framing → schema → leakage rule → feature family request → output as a comparison table + code. The leakage rule is the most important addition. State explicitly the cutoff time, the row's as-of date, and any forbidden columns. This single sentence keeps the AI from inventing features that would never be available in production.

For target-aware features, always require a "compute on training fold only, transform on validation/test" implementation. Phrase it as: "Use sklearn's TargetEncoder inside a Pipeline so encoding is fit only on training folds during cross-validation."

5. Step-by-Step Breakdown

1. State the prediction target and prevalence. Different targets favour different feature families — count outcomes love rate features, while binary classification loves ratios and interactions.
2. Define the observation point. Every feature must be computable as of a specific timestamp. State that cutoff explicitly.
3. List banned columns. Any column that contains future information, or anything that won't be available at prediction time. Banning is more reliable than allowing.
4. Ask for a feature menu first. Get 10–20 candidates in a table with signal estimates and leakage tags before writing code. Cheap iteration here saves expensive training runs later.
5. Generate code for the shortlist. Once you have picked features, ask for vectorised Pandas code keyed on the same primary key as the training data.
6. Validate with a leakage smoke test. Ask AI: "For each feature, prove it is computable strictly before observation_date." A second pass catches what the first pass missed.

Tip: Save your "feature menu" prompts per problem domain. The first time you build churn features, you spend an hour. The next time, you paste the menu and finish in ten minutes.

6. Practice Exercises

7. Key Takeaways