## 1 Introduction
### Prompt Engineering for Enhancing MLLM Performance  
To mitigate the hallucination problem and enhance the performance of MLLMs, various prompt engineering (PE) techniques have been proposed, similar to those developed for LLMs [14; 15; 16]. However, unlike LLMs, which rely solely on textual inputs, MLLMs process visual content in addition to text. As a result, strategic prompt design must go beyond simple text-based prompting and consider alignment with visual information to ensure coherence and accuracy in multimodal reasoning. First, in-context learning (ICL) [17] requires providing relevant examples within a given multimodal image-text pair context to enable the model to generate appropriate responses. Chain of thought (CoT) [18] should guide the model to solve complex reasoning tasks by leveraging sequential textual explanations based on image analysis. Similarly, step-by-step reasoning (SSR) [19] encourages the model to perform spatial and stepwise visual analysis, ensuring a structured reasoning process. Tree of thought (ToT) [19] extends this concept by considering multiple cognitive pathways derived from the image, allowing the model to select the most reliable response based on different analytical perspectives. On the other hand, retrieval-augmented generation (RAG) [20] enhances multimodal understanding by retrieving external knowledge related to the given image, enabling the model to generate evidence-based responses even when dealing with previously unseen information. In summary, prompt engineering in MLLMs must evolve beyond simple text-based design to strategically integrate visual information, ensuring that the model effectively utilizes multimodal inputs to improve response accuracy and reliability. In fact, existing prompt engineering research aimed at mitigating hallucination has predominantly focused on LLMs, while the systematic optimization of PE strategies for multimodal data remains underdeveloped. However, hallucination phenomena arising specifically from multimodal inputs present challenges that cannot be fully addressedby conventional approaches alone. Therefore, the development of prompt engineering techniques tailored to multimodal data is essential for generating accurate and contextually grounded responses. This study aims to develop an optimal prompt engineering strategy that maximizes user satisfaction and response accuracy in practical MLLM service deployment while minimizing computational resource requirements. Specifically, instead of performing additional fine-tuning on pre-trained modality encoders, pre-trained LLMs, or cross-modality transformers, we explore how multimodal-specific prompt engineering techniques alone can enhance MLLM performance. To achieve this, we systematically investigate the application of RAG, CoT, ICL, SSR, and ToT as effective prompt engineering strategies. In particular, we evaluate the impact of these techniques on state-of-the-art MLLMs, including Phi [21, 22], Llama [23], Pixtral [24], and Qwen [25, 26], providing empirical insights into their effectiveness across different architectures. To ensure that our evaluation closely aligns with user satisfaction, we employ a diverse set of performance metrics, including bilingual evaluation understudy (BLEU), recall-oriented understudy for gigus eval (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), sentence-bidirectional encoder representations from transformers (S-BERT), MoverScore, and consensus-based image description evaluation (CIDEr). These metrics collectively assess the quality, fluency, and relevance of multimodal-generated responses. For benchmark datasets, we utilize MathVista [27], CVBench [28], ScienceQA [29], nocaps [30], MSCOCO [31], and Flickr30k [32]. These datasets span a variety of domains and multimodal tasks, allowing for a comprehensive analysis of prompt engineering strategies in multimodal natural language generation.