Journal of Intelligent Computing Hathairat Ketmaneechairat 17 4 2026 https://doi.org/10.6025/jic/2026/17/4/200-222 https://www.dline.info/jic/fulltext/v17n4/jicv17n4_2.pdf 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.