# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 1 Introduction  
Conversational systems are advanced artificial intelligence (AI) tools designed to interact with users through natural language. These systems are widely used in applications such as virtual assistants, customer service chatbots, and tools for education and healthcare [1]. By enabling machines to understand and respond to human language, conversational systems have become an essential part of modern technology. Over the years, significant advancements in Natural Language Processing (NLP) have enhanced the ability of these systems to generate human-like responses [2]. However, building conversational systems for low-resource languages, such as Swahili, remains a considerable challenge due to the limited availability of large, high-quality datasets. Swahili, spoken by over 100 million people in East Africa, including Tanzania, Kenya, and Uganda, is a linguistically rich and culturally significant language but is underrepresented in the field of NLP [3]. Unlike widely spoken languages such as English or Chinese, which benefit from abundant digital resources, Swahili lacks the extensive datasets necessary for training advanced language models. This scarcity of data affects various aspects of NLP, such as syntactic parsing, semantic analysis, and named entity recognition. Moreover, the complex linguistic features of Swahili, including its extensive noun class system, agglutinative morphology, and verb conjugations, add to the challenge of creating effective language models. The absence of domain-specific corpora further limits the development of conversational systems capable of generating accurate and contextually relevant responses for Swahili speakers. As a result, Swahili speakers face significant challenges in accessing the benefits of modern conversational AI tools. These challenges include the inability to access localized customer service tools, limited access to Swahili-enabled educational applications, and a lack of support for everyday queries in digital assistants. The lack of digital inclusion restricts Swahili speakers from fully participating in technological advancements, contributing to a growing digital divide. Retrieval-Augmented Generation (RAG) offers a promising solution to these challenges by enhancing conversational systems, even in low-resource settings. RAG combines two key components: large language models (LLMs) and retrieval systems. LLMs are trained on vast amounts of text to generate fluent and coherent responses, while retrieval systems fetch relevant information from external databases [4; 5]. Together, these technologies allow RAG-based systems to incorporate external knowledge dynamically, making their responses more accurate and context-aware. For example, if a user asks a question about Swahili culture, a RAG system can retrieve information from a relevant database and use it to craft a well-informed answer. This capability is particularly valuable for languages like Swahili, where training data alone are insufficient to achieve high-quality conversational performance. This paper focuses on leveraging RAG to develop a Swahili language conversation system, addressing the challenges of limited data and improving the quality of conversational AI for Swahili speakers. The study involves preparing a Swahili-specific dataset, implementing a RAG framework, and evaluating its performance using metrics such as BLEU, METEOR, Query Performance, and inference time. Four models--mT5, GPT-2, mBART, and GPT-Neo--are tested to compare the effectiveness of RAG against traditional fine-tuning methods. The results demonstrate that RAG significantly improves response quality by retrieving and utilizing external knowledge. Although RAG introduces longer response times, its benefits in accuracy and relevance make it an effective approach for building Swahili conversational systems [6; 7]. This paper highlights the broader potential of RAG for other low-resource languages facing similar challenges.