विधियों की तुलना करें

	प्रॉम्प्ट इंजीनियरिंग	कुछ-शॉट पाठ वर्गीकरण
क्षेत्र	पाठ खनन	पाठ खनन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2020 (few-shot prompting); 2022 (chain-of-thought)	—
प्रवर्तक	Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)	—
प्रकार	NLP pipeline — structured instruction design for large language models	NLP text-classification task (low-resource)
मौलिक स्रोत	Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.	Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.
उपनाम	instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering)	few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)
संबंधित	7	4
सारांश	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.	Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.