## 2 Related Works
Additionally, Palm-2 augments the system's capacity to deliver personalizedresponses by assimilating user interactions, enhancing the quality of the information provided [12]. Research on RAG methodologies has advanced considerably in recent years. RAG offers a method to improve the efficacy of generative models by integrating retrieval systems with large language models. Numerous studies have investigated the capabilities and challenges of RAG in various contexts. [PERSON] et al. (2023) presented the RAGA framework to assess RAG pipelines without ground truth annotations, facilitating a more rapid and efficient evaluation of these systems [13]. [PERSON] (2022) illustrated how RAG can enhance models' scalability and knowledge retrieval when addressing heterogeneous knowledge sources, offering potential solutions to challenges in single-source knowledge retrieval [14]. [PERSON] (2023) examined the application of RAG in low-code development environments, demonstrating that incorporating domain-specific knowledge into LLMs can enhance developer efficiency by delivering more precise and pertinent information retrieval [15]. Furthermore, [PERSON] et al. (2023) introduced the RGB benchmark to methodically assess the efficacy of RAG across various languages and models, pinpointing substantial opportunities for enhancement in information integration and mitigating hallucinations in LLM outputs [16]. Other researchers have implemented RAG in particular domains. [PERSON] and [PERSON] (2023) investigated its application in medical education, creating a summarization framework capable of managing extensive, unstructured datasets while ensuring precision and minimizing hallucinations [17]. [PERSON] et al. (2023) presented the GAR-meets-RAG paradigm, which exhibited improved retrieval accuracy and generation in zero-shot information retrieval tasks, a vital factor in augmenting the precision of AI responses in knowledge-deficient contexts [18]. [PERSON] et al. (2020) offered essential insights into integrating the RAG model with Wikipedia to improve its knowledge-intensive tasks, including open-domain question answering [19]. Palm-2, an advanced language model, is optimized for extensive and multilingual tasks, demonstrating notable enhancements in performance, efficiency, and reasoning abilities. Palm-2 surpasses its predecessor PaLM in numerous reasoning tasks, notably improved multilingual capabilities and computational efficiency [20]. [PERSON] et al. (2022) introduce PaLM, a language model created via Pathways to attain high efficiency and exceptional performance on benchmarks, particularly in few-shot learning tasks [21]. The Med-PaLM 2 model, an iteration of Palm-2, exhibited remarkable efficacy in medical question-answering tasks, achieving a significant performance milestone by exceeding human-level performance across various medical datasets [22]. PaLM-E is an embodied multimodal language model that integrates real-world sensor modalities with language comprehension, essential for addressing intricate robotic and reasoning challenges [23]. Additional applications of Palm-2 are apparent in biological domains, where it has been employed to analyze gene-phenotype associations for genetic discovery, utilizing AI to formulate innovative hypotheses [24].