Sammenlign metoder

	Prompt Engineering	Zero-Shot Klassifikation
Fagområde	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2020 (few-shot prompting); 2022 (chain-of-thought)	2019
Ophavsperson	Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)	Yin, Hay & Roth
Type	NLP pipeline — structured instruction design for large language models	NLP text-classification task
Oprindelig kilde	Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.	Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923.
Aliasser	instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering)	zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification)
Relaterede	7	3
Resumé	Prompt engineering is the practice of crafting structured natural-language instructions — prompts — to elicit targeted outputs from large language models (LLMs). Formalised by Brown et al. (2020) in the context of GPT-3 and extended by Wei et al. (2022) with chain-of-thought prompting, it encompasses four main strategies: zero-shot, few-shot, chain-of-thought, and tree-of-thought. Rather than re-training a model, the analyst shapes the model's behaviour entirely through the design of the input text.	Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets.