Sammenlign metoder

	Prompt Engineering	Tekstklassifisering
Fagfelt	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2020 (few-shot prompting); 2022 (chain-of-thought)	—
Opphavsperson	Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)	—
Type	NLP pipeline — structured instruction design for large language models	Supervised NLP classification task
Opprinnelig kilde	Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering)	text categorization, document classification, topic classification, metin sınıflandırma
Relaterte	7	4
Sammendrag	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.