GoKawiilAnalysis Argues LLMs Make Poor Classifiers but Strong Feature Extractors
A technical essay examines why using large language models directly as classifiers is frustrating despite decent performance, citing problems with calibration, inability to reliably use structured data or context, and limited interpretability of prompt-following behavior. The author suggests reframing the role of LLM outputs entirely rather than treating them as final classification decisions.
GoKawiil's interpretation of the reporting above, not reported fact.
As more teams bolt LLM prompts onto classification tasks, understanding their structural limitations—poor calibration, opaque reasoning, and inconsistent use of provided context—helps prevent overreliance on outputs that look authoritative but lack statistical rigor. Reframing LLM outputs as engineered features rather than final labels could lead to more robust, combinable pipelines with traditional ML calibration techniques.
- LLM classifiers lack reliable calibration and confidence estimates for precision-recall tradeoffs.
- LLMs may ignore structured data or context provided in prompts, losing useful signal.
- The essay proposes treating LLM outputs as feature engineering inputs rather than standalone classification labels.
O'Reilly Hands-On Machine Learning book — If you're wrestling with LLM classifiers and calibration issues, this book grounds you in the classical ML techniques—thresholding, feature engineering, calibration curves—that make classifiers reliable. It's a great reference for turning LLM outputs into proper engineered features for a real classifier pipeline.
See O'Reilly Hands-On Machine Learning book on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: minimallysufficient.com — Taylor Pospisil, 2026-09-17
Published there as: “LLM Classification Is Feature Engineering”
Read the original report → The summary and analysis above are GoKawiil's own, written from reporting by the source above. Facts and quotes belong to the original publisher.