The performance of a large language model is significantly influenced by the quality of its prompt. While the importance of clear task specification is a long-standing principle in Natural Language Processing (NLP), it has become even more vital for contemporary LLMs, where minor variations in input can significantly impact accuracy (Zhao et al. 2021). Effective prompt design, therefore, relies on a set of evidence-based principles, primarily that prompts must be clear, specific, and structured.
Clarity ensures the researcher's intent is legible to the model. Experiments show that LLMs are highly sensitive to the explicit structure of a task. Clear prompts use stable headings and separators to distinguish between inputs and outputs, and they state the task and its success criteria upfront. This helps the model correctly parse the request even when provided data is noisy, as the model learns to exploit the format and structure of the prompt itself (Min et al. 2022).
Specificity narrows the model's vast potential solution space to what is relevant for the task. Being explicit about the domain, audience, and constraints (e.g., length, tone) reduces unwanted variance in the output (Zhao et al. 2021). However, more context is not always better. Research on long-context models reveals a robust “lost-in-the-middle” effect, where models often under-utilise information placed in the middle of a long prompt (Liu et al. 2023). Specificity, therefore, also means being concise: keeping vital facts close to the main instruction and avoiding unnecessary prose.
Structure provides a procedural scaffold for the model’s reasoning process. Techniques like Chain-of-Thought (CoT) prompting improve performance on complex tasks by eliciting intermediate rationales (Wei et al. 2022). Even a minimal cue, such as the phrase “Let’s think step by step,” can unlock this step-by-step reasoning capability (Kojima et al. 2022). More advanced methods like Plan-and-Solve or Least-to-Most prompting further refine this by decomposing a complex problem into a sequence of simpler sub-problems (Wang et al. 2023; Zhou et al. 2022).
In sum, clarity, specificity, and structure are not merely stylistic suggestions but core methodological principles for interacting with LLMs. A researcher must treat the prompt with the same rigour as any other part of their methodology: documenting its design, justifying its components, and iterating based on measured outcomes. While this approach offers significant control, two key limitations must be acknowledged: prompting cannot create semantic understanding where none exists, and long-context models still exhibit attention deficits that require careful structural mitigation (Liu et al. 2023).
References:
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2. Liu, Nelson F., Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni & Percy Liang. 2023. Lost in the middle: How language models use long contexts. arXiv preprint arXiv:2307.03172. Available at: https://arxiv.org/abs/2307.03172 ^ Back
3. Min, Sewon, Mike Lewis, Luke Zettlemoyer & Hannaneh Hajishirzi. 2022. Rethinking the role of demonstrations: What makes in-context learning work? In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 8925–8948. Association for Computational Linguistics. Available at: https://aclanthology.org/2022.emnlp-main.759/ ^ Back
4. Wang, Lei, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee & Ee-Peng Lim. 2023. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. arXiv preprint arXiv:2305.04091. Available at: https://arxiv.org/abs/2305.04091 ^ Back
5. Wei, Jason, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V. Le & Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35: 24824–24837. ^ Back
6. Zhao, Zihao, Eric Wallace, Shi Feng, Dan Klein & Sameer Singh. 2021. Calibrate before use: Improving few-shot performance of language models. In International Conference on Machine Learning, 12697–12706. PMLR. ^ Back
7. Zhou, Denny, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans et al. 2022. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625. Available at: https://arxiv.org/abs/2205.10625 ^ Back
