Stop Asking AI for Just the 'Answer'? It's Time to Use It as a 'Data Chef'

By Gi Pyeong Lee
AI LLM DataAnalysis MachineLearning TechTrends
An image symbolizing the process of complex text data being transformed into organized tabular data via AI.
AI Summary

Instead of using LLMs as final destinations for data classification, we introduce a new paradigm of using them as 'Feature Engineering' tools that maximize the performance of predictive models by structuring complex, unstructured data.

Imagine this: You have tens of thousands of customer consultation logs piled up on your desk. It feels overwhelming to read through them one by one. In the past, we would command AI to “classify the content of this consultation” and simply receive the result. However, in the field of AI recently, this process has begun to be viewed not just as simple “classification,” but as “cooking” that adds greater value to the data.

It is about not just trusting the results AI provides, but utilizing its delicate ability to grasp context as a tool for “Feature Engineering,” which makes data more palatable. Feature engineering here refers to the task of processing data into core information that machine learning models can easily understand.

Why is this important?

Until now, for us, Large Language Models (LLMs) were smart assistants that answered questions or wrote text. However, in practical fields, the “knowledge” used to provide those answers often holds much greater value than the answers themselves.

If you use AI solely as a classifier, you are helpless when the AI provides the wrong answer; but if you use AI as a data processor, it is different. When you run traditional machine learning models (e.g., XGBoost) based on the structured information extracted by AI, predictive accuracy increases dramatically. In other words, AI is no longer the protagonist of prediction, but is becoming the most powerful “assistant” that makes predictions more accurate [Source: LLM Classification Is Feature Engineering Minimally Sufficient](https://minimallysufficient.com/posts/llm-classification-is-feature-extraction/) Source: Stop Labeling, Start Engineering: The New Era of LLM ….

Easy to understand: AI is an excellent translator

Does the term feature engineering sound difficult? By way of analogy, AI is a very excellent “translator.” Imagine you have to read a very complex and messy foreign language document and summarize the core content into a table.

  • Traditional method (classification): You ask AI, “Tell me if this document is positive or negative,” and it simply attaches a single label like “positive.” The rest of the rich information is discarded.
  • New method (feature engineering): You use AI as a smart translator. The AI reads the document and extracts key information: “This customer is dissatisfied with delivery speed, satisfied with the price, and has an intention to repurchase.” Then, it organizes this into items like “delivery satisfaction” and “price score.”
Information organized in this way becomes the perfect form for a computer to understand. [Source: Feature engineering from LLM outputs Xgboost Advanced Course The Neural Base](https://theneuralbase.com/xgboost/learn/advanced/feature-engineering-from-llm-outputs/). In this process, the logical reasoning process that AI uses to reach its classification conclusion itself becomes the core feature of the data Source: Stop Labeling, Start Engineering: The New Era of LLM ….

Current situation: How far have we come?

Relevant technologies are already being actively applied in the field.

  1. Automated feature discovery: Frameworks like FeatLLM or LLM-FE utilize AI’s knowledge and reasoning capabilities to automatically discover data features that are difficult for humans to find one by one Source: Large Language Models Can Automatically Engineer Features for … Source: LLM-FE: Automated Feature Engineering for Tabular Data with ….
  2. Dramatic improvement in performance: According to research results, when data was processed based on LLMs, the performance of traditional machine learning models improved overwhelmingly. In one study, it proved the best performance by recording the lowest rank (1.47) across 19 datasets Source: LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers [Quick Review]. There is even a case where the Brier Score, a predictive error metric, was nearly halved from 0.26 to 0.13 in a complex classification task [Source: LLM Classifiers: Cut Brier Score 0.26 to 0.13 explainx.ai …](https://www.explainx.ai/blog/llm-classification-feature-engineering-calibration-2026).
  3. Easy access: We are in an era where you can perform this high-level work simply with well-crafted prompts (commands), without needing to re-train the model from scratch (fine-tuning) Source: How to UseLLMforClassification.

What will happen in the future?

In the future, rather than building AI models from scratch, the most important skills for an engineer will be “which AI to use as a data processor” and “how to ask questions so that AI understands the data better.” Methods that create features through reasoning results, such as FeRG-LLM (which demonstrated more efficient and superior performance than existing large models), are expected to become the mainstream Source: FeRG-LLM : Feature Engineering by Reason Generation Large Language Models [Quick Review].

Data will no longer be raw material itself; it will be essential to go through the process of being refined into jewels via the sophisticated tool that is AI.

MindTickleBytes AI Reporter’s Perspective

An LLM is not a “test machine” that gets the right answer; it is a “microscope” that discerns what is important. We should not be satisfied with AI’s answers, but should borrow AI’s “eyes” that find those answers to make our data more valuable.

References

  1. [LLM Classification Is Feature Engineering Minimally Sufficient](https://minimallysufficient.com/posts/llm-classification-is-feature-extraction/)
  2. Stop Labeling, Start Engineering: The New Era of LLM …
  3. [LLM Classifiers: Cut Brier Score 0.26 to 0.13 explainx.ai …](https://www.explainx.ai/blog/llm-classification-feature-engineering-calibration-2026)
  4. Large Language Models Can Automatically Engineer Features for …
  5. LLM-FE: Automated Feature Engineering for Tabular Data with …
  6. LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers [Quick Review]
  7. [Feature engineering from LLM outputs Xgboost Advanced Course The Neural Base](https://theneuralbase.com/xgboost/learn/advanced/feature-engineering-from-llm-outputs/)
  8. FeRG-LLM : Feature Engineering by Reason Generation Large Language Models [Quick Review]
  9. How to UseLLMforClassification
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Test Your Understanding
Q1. What is the most important 'actual power' in the classification process using LLMs?
  • The size of the model
  • The final label used to classify data
  • The reasoning process the LLM uses to classify data
Recent studies emphasize that the 'reasoning process' used to reach a conclusion plays a more key role in structuring complex data than the labels themselves generated by the LLM.
Q2. What is the core goal pursued by frameworks like LLM-FE?
  • Automated feature discovery without human intervention
  • Reducing the size of LLM models
  • Reducing data labeling costs
Tools like LLM-FE focus on automatically discovering features suitable for tabular data by leveraging the knowledge and reasoning capabilities of LLMs.
Q3. Which of the following is a correct advantage of feature engineering using LLMs?
  • Machine learning models are no longer needed
  • Improved interpretability and accuracy of predictive models
  • Complete elimination of the data cleaning process
Utilizing LLMs not only enhances the predictive power of existing machine learning models but also makes the basis for predictions easier to understand, thereby improving interpretability.
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