Turning Raw Text into Structured Features: How LLMs Are Reshaping Tabular Data Preparation
Feature engineering has long been one of the more tedious parts of building machine learning pipelines — especially when your dataset mixes structured numbers with messy, unstructured text. A growing number of practitioners are now routing that problem through large language models, using them not as the final predictor, but as a preprocessing layer that converts raw text into clean, structured columns a traditional classifier can actually use.
From Text to Tables: Feature Engineering with LLMs for Tabular Data
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This tutorial walks through exactly that workflow: taking a dataset with mixed text and numeric fields, using a Groq-hosted LLaMA model to extract structured JSON features from the text columns, and then training a scikit-learn classifier on the resulting tabular data. The stack is practical and reproducible — Pydantic for schema enforcement, the OpenAI-compatible Groq client for inference, and a Random Forest as the downstream model.
Why LLMs Make Sense as a Feature Extraction Layer
Traditional NLP feature engineering — TF-IDF, bag-of-words, manual regex patterns — works, but it requires domain knowledge upfront and tends to be brittle. You have to anticipate what signals matter before you write a single line of code. LLMs flip that dynamic. Because they've been trained on vast corpora, they already carry implicit understanding of sentiment, urgency, topic categorization, and entity types. You can prompt them to surface those signals as structured output without hand-crafting extraction rules.
The Groq-hosted Llama family is a practical choice here for a specific reason: Groq's inference hardware delivers low-latency responses, which matters when you're running an LLM call per row across a dataset. Pairing that with Pydantic's BaseModel and Field gives you schema validation on the output — so instead of hoping the model returns parseable JSON, you're enforcing a contract. If the model drifts from the expected structure, Pydantic catches it before it corrupts your feature matrix.
Building the Dataset and Extraction Pipeline
The example uses a toy dataset built around support tickets — a natural fit because tickets combine free-form text descriptions with numeric metadata like response times or priority scores. The goal is binary classification: predicting some outcome (escalation, resolution category, etc.) from both the text and numeric signals together.
The import block sets up the full pipeline in one place:
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import pandas as pd import json from pydantic import BaseModel, Field from openai import OpenAI from google.colab import userdata from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.preprocessing import StandardScaler |
The Groq client is initialized using the OpenAI-compatible interface — meaning you point the base URL at Groq's API endpoint and authenticate with a Groq API key stored in Colab's userdata secrets. From there, the Pydantic schema defines exactly what fields the LLM should extract from each ticket: things like sentiment score, urgency level, topic category, and whether specific entity types are mentioned. Each field gets a description that doubles as a prompt hint, nudging the model toward consistent output.
For each row in the dataset, the pipeline sends the ticket text to the LLaMA model with a structured output instruction, parses the validated JSON response, and appends those extracted fields as new columns alongside the original numeric data. The result is a fully tabular feature matrix — no embeddings, no dense vectors, just interpretable columns that a Random Forest can split on directly.
What This Means for Hybrid ML Pipelines
The broader implication here goes beyond this specific example. Most real-world datasets aren't purely numeric or purely text — they're hybrid. Customer records have free-form notes next to account ages and transaction counts. Medical datasets combine clinical measurements with physician observations written in natural language. Log files mix structured fields with unstructured error messages.
The conventional approach has been to handle these modalities separately: train one model on the numeric features, another on text embeddings, then ensemble them. That works, but it adds architectural complexity and makes the pipeline harder to maintain. Using an LLM as a feature extractor collapses that into a single tabular representation, which means you can use any standard classifier without needing to manage multi-modal model fusion.
There are real tradeoffs to acknowledge. LLM-based extraction adds latency and API cost per row, which doesn't scale cheaply to millions of records. The quality of extracted features also depends heavily on prompt design and model consistency — a poorly specified schema can produce noisy or inconsistent columns that hurt downstream model performance more than they help. And unlike learned embeddings, the features you get back are only as good as what you thought to ask for.
Still, for datasets in the thousands to low hundreds of thousands of rows — which covers a large share of real enterprise ML problems — this approach offers a compelling tradeoff: faster iteration, more interpretable features, and a simpler overall pipeline than maintaining separate text and numeric modeling tracks.
The combination of Groq's inference speed, Pydantic's output validation, and scikit-learn's classifier ecosystem makes this a surprisingly low-friction workflow to stand up, and the support ticket framing gives it an immediately recognizable real-world anchor that most ML practitioners will have encountered in some form.
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