手法を比較

	プロンプトエンジニアリング	自然言語生成
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	2020 (few-shot prompting); 2022 (chain-of-thought)	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)
提唱者	Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)
種類	NLP pipeline — structured instruction design for large language models	NLP generative task — structured data to natural language
原典	Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.	Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.
別名	instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering)	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)
関連	7	7
概要	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.	Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.