# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 2 Related Work
### Retrieval-Augmented Generation (RAG) in NLP  
Retrieval-Augmented Generation (RAG) is an innovative approach in NLP that combines the strengths of retrieval systems and large language models (LLMs). Unlike traditional language models that rely solely on pre-existing training data, RAG includes a retrieval component to dynamically fetch relevant information from external knowledge bases or databases. This additional step allows the model to provide responses that are more accurate, contextually relevant, and detailed [14]. RAG works by first identifying the most relevant information related to a user's query from an external source. This retrieved information is then incorporated into the language model, which uses it to generate a response. For example, if a user asks a question about Swahili culture, the retrieval system can find relevant content in a Swahili knowledge repository. The language model can then use this content to craft a well-informed response [15; 16]. This approach is particularly useful for low-resource languages like Swahili, where training data alone may not cover all possible queries or topics. By leveraging external knowledge, RAG can overcome the limitations of small datasets and enhance the performance of conversational systems.  
Compared to traditional fine-tuning methods, where the model's ability to answer queries is restricted by its training data, RAG offers dynamic adaptability. Fine-tuned models require retraining when new knowledge is introduced, making them less flexible for real-time or continuously evolving applications. RAG, on the other hand, can incorporate new information dynamically during retrieval, eliminating the need for frequent retraining [17]. Another alternative is knowledge-grounded conversational models, which rely on static knowledge graphs or curated databases. While these models excel in specific domains with well-defined knowledge bases, they are less effective in low-resource settings like Swahili due to the lack of comprehensive, structured knowledge repositories. RAGbridges this gap by utilizing unstructured data sources, making it more versatile in diverse domains [18]. However, RAG introduces additional computational complexity because of the retrieval step, which increases the response time compared to static models. Despite this, its ability to generate highly relevant and contextually accurate responses outweighs the computational trade-offs, especially for low-resource scenarios. For Swahili NLP, RAG has the potential to significantly improve conversational systems by dynamically retrieving missing contextual information. For instance, a RAG-based Swahili chatbot could fetch detailed explanations about historical events, cultural practices, or idiomatic expressions from a database, even if such topics were not covered in its training data. This method bridges the gap between resource availability and system performance, making it a powerful tool for building conversational systems in low-resource settings [19].  
While fine-tuning and static knowledge-based approaches have their strengths, RAG emerges as an optimal choice due to its ability to integrate unstructured external data dynamically, provide adaptability, and address data limitations in low-resource languages like Swahili.