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This paper presents the Inclusive Prompt Engineering Model (IPEM), a modular framework designed to enhance LLM performance, adaptability, and ethical alignment through prompt-level strategies alone. IPEM integrates four components: Memory-of-Thought for multi-turn consistency, Enhanced Chain-of-Thought prompting for logical verification, Structured and Analogical Reasoning modules for tabular and cross-domain tasks, and Evaluation and Feedback Loops that incorporate uncertainty-aware selection and bias mitigation mechanisms. Evaluated across tasks in arithmetic reasoning, healthcare triage, financial forecasting, and inclusive question answering, IPEM consistently improves model outputs over a GPT-4 baseline. Notable outcomes include up to twenty percentage points in accuracy gains, a 25 percent reduction in logical errors, and nearly 20 percent reduction in social bias scores, all without modifying model weights. Moreover, IPEM reduces annotation demands by one-third while preserving performance, demonstrating its utility in low-resource environments. By unifying ethical safeguards and reasoning mechanisms in a prompt-based system, IPEM offers a reproducible and auditable pathway for deploying adaptable and fair AI systems. The framework contributes both practical solutions and theoretical insights to the evolving field of prompt engineering.<\/jats:p>","DOI":"10.1007\/s10462-025-11330-7","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:14:34Z","timestamp":1755800074000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Inclusive prompt engineering for large language models: a modular framework for ethical, structured, and adaptive AI"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-6660","authenticated-orcid":false,"given":"Mohamad Saleh","family":"Torkestani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7969-3609","authenticated-orcid":false,"given":"Ali","family":"Alameer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9026-4613","authenticated-orcid":false,"given":"Shivakumara","family":"Palaiahnakote","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1539-5546","authenticated-orcid":false,"given":"Taha","family":"Manosuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"11330_CR1","doi-asserted-by":"publisher","unstructured":"Agarwal, U., Tanmay, K., Khandelwal, A., & Choudhury, M. 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