# Opinion: LLM classification is really feature engineering, not a plug‑and‑play solution

Digest AI · Research · published 2026-09-13T07:00:00Z

Canonical: https://digestai.news/story/opinion-llm-classification-is-really-feature-engineering-not-a-plugand

## Summary

The author argues that using large language models (LLMs) directly as classifiers is problematic. LLMs provide hard labels without reliable calibration, threshold control, or clear interpretability, and they struggle to incorporate structured data or adapt to different class prevalences. Consequently, they are ill‑suited for many practical classification tasks.

A proposed remedy is to treat the LLM’s verdict as just another feature and feed it into a conventional model such as logistic regression. This approach yields calibrated probabilities, allows the inclusion of additional covariates, and lets practitioners set operating thresholds to balance precision and recall. A case study on irony detection in the SemEval‑2018 tweet dataset shows that a simple LLM prompt already reaches an F1 of 0.747, while adding a logistic‑regression layer improves calibration and, with extra deterministic tweet features, pushes F1 to 0.779 and lowers the Brier score to 0.127.

The piece concludes that framing LLMs as feature engineers rather than standalone classifiers can unlock higher performance and reliability, and it points to recent research exploring automated feature generation and agentic classifiers for future work.

## Key points

- LLMs used as classifiers lack calibration, threshold control, and interpretability, making them hard to trust out‑of‑the‑box.
- Wrapping LLM outputs in a logistic regression provides calibrated probabilities, allows additional features, and improves F1 scores on irony detection to 0.779.
- Adding deterministic tweet features (length, emojis, URLs) further reduces Brier score to 0.127 and boosts F1 to 0.779.

## Why it matters

Understanding LLMs as feature generators rather than standalone classifiers lets practitioners combine them with traditional models, achieving better calibration and performance while retaining interpretability—crucial for reliable AI‑driven decision making in industry and research.

## Sources

1. [LLM Classification Is Feature Engineering](https://minimallysufficient.com/posts/llm-classification-is-feature-extraction) (minimallysufficient.com, 2026-09-13)

## Cite

Digest AI, "Opinion: LLM classification is really feature engineering, not a plug‑and‑play solution", 13 September 2026, https://digestai.news/story/opinion-llm-classification-is-really-feature-engineering-not-a-plugand

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