Journal of Data Processing Pit Pichappan 16 3 2026 https://doi.org/10.6025/jdp/2026/16/3/119-142 https://www.dline.info/jdp/fulltext/v16n3/jdpv16n3_1.pdf The rapid deployment of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) necessitates a careful balance between computational efficiency, factual accuracy, and user satisfaction. While existing research often evaluates these dimensions in isolation, this study proposes an integrated empirical framework to analyze their combined influence on overall GenAI performance. Utilizing a dataset of 1,000 simulated AI usage sessions across six state of the art models (e.g., Claude 3.7, GPT-4o, Llama 3.1) and diverse application domains, we conducted correlation analyses, Ordinary Least Squares (OLS) regression, and statistical validation tests. Results indicate that prompt length is the dominant positive predictor of response latency, significantly outweighing the impact of decoding hyperparameters like temperature and Top-P. Among the evaluated models, Claude 3.7 demonstrated the optimal balance, achieving the highest success rate and user satisfaction alongside a comparatively low hallucination rate. Notably, task-specific analysis revealed that while translation tasks yielded the highest reliability, Retrieval- Augmented Generation (RAG) tasks exhibited unexpectedly high hallucination rates, suggesting that retrieval augmentation alone does not guarantee factual grounding. Furthermore, statistical validation confirmed the superiority of a proposed multi-layered “Double Lock” guardrail framework over baseline regex methods. Ultimately, this study indicated that concise prompt engineering and task specific model selection are more critical for optimizing LLM deployment than model identity alone, providing actionable insights for developing reliable, user centric AI systems.