From Labels to Prompts: LLMs Match Supervised Warranty Classification
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Prompted LLMs, tuned through reasoning-led iteration, matched a supervised warranty classifier and shifted the bottleneck from labeled data to instructions.
Categorizing and labeling text into predefined classes using NLP techniques, from traditional ML approaches like TF-IDF with classifiers to modern LLM-based methods.
Prompted LLMs, tuned through reasoning-led iteration, matched a supervised warranty classifier and shifted the bottleneck from labeled data to instructions.

Use embeddings + vector search + DSU clustering to canonicalize LLM-generated labels, yielding consistent, cheaper, and faster classification at scale.