Prompt engineering, the deliberate crafting of inputs to guide large language models (LLMs) towards precise and effective outputs, is pivotal in harnessing AI capabilities across diverse applications, from research to creative tasks. Attention to prompts is essential because poorly constructed inputs can lead to inaccurate, irrelevant, or biased responses, wasting resources and undermining trust in AI systems. As LLMs operate on probabilistic interpretations rather than true understanding, minor flaws in prompts can amplify errors, highlighting the need for vigilance to optimise performance and mitigate risks.
Vagueness represents a fundamental pitfall, wherein prompts lack sufficient specificity, leading to outputs that deviate from intended objectives due to the model's reliance on training data distributions rather than nuanced comprehension. Empirical research highlights how ambiguous phrasing exacerbates issues like hallucinations or irrelevant responses, as LLMs interpret inputs probabilistically, often amplifying biases or overgeneralising (Errica et al. 2024). For example, a prompt such as "Discuss renewable energy" might elicit a superficial summary of solar and wind sources, neglecting economic viability or policy dimensions, thereby yielding incomplete or misaligned content. To counteract this, prompts should embed precise parameters, such as scope, audience, and evidence requirements. A refined version could be: "Analyse the economic barriers to renewable energy adoption in developing economies, citing peer-reviewed studies from 2020 onwards, and propose two policy interventions." This specificity fosters structured, evidence-based outputs, potentially reducing sensitivity to minor variations—a common instability noted in classification tasks. Scholars advocate role-playing techniques, instructing the model to adopt an expert persona, to further delineate expectations and minimise ambiguity.
Omitting contextual details deprives LLMs of the scaffolding needed for coherent reasoning, often resulting in generic or erroneous outputs disconnected from the user's domain. Studies on prompt knowledge gaps reveal that missing context—such as project specifics or prior interactions—correlates with higher rates of unsuccessful resolutions, with up to 44.6% of flawed prompts exhibiting this deficiency in software issue threads (Ehsani et al. 2025). An illustrative prompt like "Interpret these results" sans data might prompt assumptions of a basic dataset, overlooking nuances like temporal trends and producing superficial analyses. Remediation involves explicit contextual integration: "Using the attached dataset on urban traffic patterns (e.g., hourly vehicle counts: 08:00–200; 09:00–450; 10:00–300), interpret peak-hour anomalies, attribute causes such as rush-hour congestion, and suggest mitigation strategies." This approach enhances reasoning depth, aligning with chain-of-thought methodologies that decompose tasks for improved accuracy. Multi-turn interactions, where context accumulates iteratively, further simulate human dialogue, reducing gaps and bolstering reliability.
Information overload occurs when prompts incorporate excessive elements, diluting focus and straining model token limits, which can lead to fragmented or truncated responses. SWOT analyses of prompting techniques identify this as a weakness, particularly in iterative or multimodal methods, where complexity hinders integration and introduces noise (Singh et al. 2024). For instance, "Examine AI ethics, encompassing bias mitigation, privacy concerns, regulatory frameworks, societal impacts, technological solutions, case studies from healthcare and finance, and future trends compared to historical precedents" may overwhelm the model, resulting in superficial coverage or omissions. Strategies for avoidance include modular decomposition: Begin with "Outline key ethical biases in AI systems, focusing on algorithmic discrimination in hiring tools." Subsequent prompts can expand, ensuring manageability. Specifying formats—e.g., numbered lists—organises outputs, as supported by evaluations showing segmented tasks yield lower error rates and higher consistency (Joshi et al. 2025).
Disregarding LLMs' inherent constraints, such as biases from training data or inability to access real-time knowledge, invites biased or inaccurate outputs, particularly in sensitive queries. Literature critiques prompting as an unreliable interface, prone to stochastic variations and ethical lapses like amplified prejudices. A prompt like "Recommend the optimal investment strategy for 2025" risks fabricating trends based on outdated data, potentially endorsing volatile assets without factual grounding (Morris 2024). Neutral framing mitigates this: "Drawing on economic reports up to 2023, summarise balanced investment strategies in volatile markets, highlighting risks and diversification benefits." Instructing multi-perspective consideration counters biases, aligning with recommendations for fairness audits in prompt design (He et al. 2025).
Neglecting iteration and format specifications yields inconsistent results, underscoring prompt engineering's experimental essence. User studies demonstrate that non-experts often abandon refinement after initial failures, with only 45% achieving accuracy gains in data labelling tasks due to trial-and-error inefficiencies. For example, "Brainstorm marketing campaigns" might generate unstructured ideas lacking viability assessments. Enhanced practices incorporate few-shot examples and directives: "Brainstorm three marketing campaigns for eco-friendly products, formatted as: Campaign Name – Target Audience – Key Tactics – Projected Impact." Iterative testing, informed by alignment scores, refines outputs, as evidenced by tools facilitating systematic evaluation (Zamfirescu-Pereira et al. 2023).
In sum, the pitfalls in prompt engineering—vagueness, contextual deficits, overload, limitation ignorance, and iterative neglect—systematically undermine LLM efficacy, perpetuating inefficiencies and ethical risks. By dissecting these through examples and scholarly insights, this essay illuminates pathways to remediation, emphasising precision, modularity, and empirical iteration. As LLMs proliferate, cultivating awareness of these issues is imperative for fostering robust AI ecosystems. Future scholarship should prioritise automated optimisation frameworks to democratise proficient prompt design, ensuring equitable access and minimising human error.
References:
1. Ehsani, Ramtin, Sakshi Pathak, and Preetha Chatterjee. 2025. “Towards Detecting Prompt Knowledge Gaps for Improved LLM-Guided Issue Resolution.” In 2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR), 699–711. IEEE. ^ Back
2. Errica, Federico, Giuseppe Siracusano, Davide Sanvito, and Roberto Bifulco. 2024. “What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering.” arXiv preprint arXiv:2406.12334. ^ Back
3. He, Zeyu, Saniya Naphade, and Ting-Hao Kenneth Huang. 2025. “Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–33. ^ Back
4. Joshi, Ishika, Simra Shahid, Shreeya Manasvi Venneti, Manushree Vasu, Yantao Zheng, Yunyao Li, Balaji Krishnamurthy, and Gromit Yeuk-Yin Chan. 2025. “Coprompter: User-Centric Evaluation of LLM Instruction Alignment for Improved Prompt Engineering.” In Proceedings of the 30th International Conference on Intelligent User Interfaces, 341–365. ^ Back
5. Morris, Meredith Ringel. 2024. “Prompting Considered Harmful.” Communications of the ACM, Available at: https://cacm.acm.org/opinion/prompting-considered-harmful/ ^ Back
6. Singh, Aditi, Abul Ehtesham, Gaurav Kumar Gupta, Nikhil Kumar Chatta, Saket Kumar, and Tala Talaei Khoei. 2024. “Exploring Prompt Engineering: A Systematic Review with SWOT Analysis.” arXiv preprint arXiv:2410.12843. https://arxiv.org/abs/2410.12843 ^ Back
7. Zamfirescu-Pereira, J. Diego, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. “Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–21. ^ Back
