@article{4837,
  author = {Hathairat Ketmaneechairat},
  title = {Prompt Clarity, Hallucination Risk, and Trustworthy LLM Performance: Mediation Analysis, Cross-Lingual/Domain Robustness, and Routing Gains},
  journal = {Journal of Intelligent Computing},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jic/2026/17/4/200-222},
  url = {https://www.dline.info/jic/fulltext/v17n4/jicv17n4_2.pdf},
  abstract = {Despite the rapid advancement of Large Language Models (LLMs), hallucinations remain a critical barrier to
their trustworthy deployment in high stakes domains. This study examines how prompt clarity influences
LLM response quality and trustworthiness, focusing on the mediating role of hallucination risk. Utilizing a
synthetic benchmark of 15,000 prompt response pairs spanning five languages, multiple domains, four
difficulty levels, and five model families, we employed causal style mediation analysis, fairness audits, and
routing policy simulations. Results indicate that hallucination risk is the dominant mediator, accounting for
approximately 66% to 71% of the total effect of prompt clarity on response quality. While LLM performance
demonstrated strong cross-lingual and cross-domain robustness, trustworthiness measured via a novel
Trustworthy Response Index (TRI) declined significantly as task difficulty increased. Furthermore, model
family significantly moderated the mediation pathway; Reasoning-LLMs showed greater resilience to prompt
ambiguity than other families. System level simulations revealed that intelligent model routing, particularly
strategies prioritizing Reasoning LLMs, yields substantial gains in overall quality and TRI. These findings
underscore that prompt engineering and model aware routing are interconnected determinants of reliable
AI, necessitating integrated strategies for trustworthy LLM deployment.},
}