Feature Engineering with LLMs: Techniques & Python Examples

2026-05-07 · Source: Analytics Vidhya · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

Feature engineering with Large Language Models (LLMs) automates the creation of rich, semantic features from raw, unstructured data, moving beyond traditional manual, context-agnostic methods. This approach leverages LLMs to understand language, extract meaning, and generate high-dimensional representations that capture relationships and subtle nuances often missed by techniques like TF-IDF. Key techniques include using LLM-generated embeddings as dense semantic features, prompt-based extraction for specific structured information, schema-guided extraction for consistent outputs, semantic feature generation for new descriptive attributes, and context-aware feature creation. The article also details hybrid feature spaces, combining tabular data with text embeddings, and presents an end-to-end workflow for sentiment classification achieving 0.95 accuracy. While offering significant benefits in NLP, tabular ML, and domain-specific applications, challenges include reliability, reproducibility, potential bias, and the risk of over-reliance on LLM-generated features.

Key takeaway

For AI Engineers and Data Scientists building machine learning pipelines, integrating LLM-based feature engineering can significantly enhance model performance, especially with unstructured data. You should explore techniques like semantic embeddings and prompt-based extraction to generate richer features, but critically evaluate LLM outputs for bias and ensure reproducibility. Combine these advanced features with your existing domain-specific features to build more robust and scalable AI systems.

Key insights

LLMs automate semantic feature engineering, transforming raw data into context-rich representations for improved ML performance.

Principles

• Semantic features outperform manual features for complex tasks.
• Combine LLM features with domain features for robust models.

Method

Utilize LLMs for embedding generation, prompt-based extraction, schema-guided output, semantic attribute generation, and context-aware feature creation, integrating these with traditional features in hybrid pipelines.

In practice

• Use `SentenceTransformer('all-MiniLM-L6-v2')` for embeddings.
• Employ `google/flan-t5-base` for prompt-based feature extraction.
• Combine tabular data with LLM embeddings using `np.hstack`.

Topics

• Feature Engineering with LLMs
• Semantic Features
• Embeddings
• Prompt-Based Feature Extraction
• Schema-Guided Extraction

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Analytics Vidhya.