Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions.

Large Language Models change this by helping machines understand language, extract meaning, and generate richer features automatically. This shift opens new ways to build smarter ML pipelines. This article offers a practical guide to feature engineering using LLMs.

Table of contents

What is Feature Engineering with LLMs?

The Shift: From Manual Features to Semantic Features

Core Techniques in Feature Engineering with LLMs Embeddings as Features

Prompt-Based Feature Extraction

Schema-guided extraction

Semantic Feature Generation

Context-Aware Feature Creation

Hybrid Feature Spaces (Multi-Modal Pipelines) Combining Tabular, Text, and Embeddings

Multi-Modal Feature Pipelines

End-to-End Flow (Data → LLM → Features →…