Prompt-based engineering methods, including instruction prompt tuning (IPT), are gaining traction for adapting downstream tasks into large language model (LLM) formats. While most studies focus on in-domain knowledge transfer, there is limited exploration of IPT for out-of-domain (OOD) transfer, where tasks differ contextually. To address this gap, a novel LLM-based framework is proposed to enhance interpretability and to improve OOD transferability for unseen tasks. This approach leverages computational intelligence techniques to refine IPT, enabling LLMs to enhance their performance in detecting contextually dissimilar depression cases within text-based conversational data. This refinement broadens their effectiveness in diverse and challenging scenarios.