The paper explores the potential and challenges of Small Language Models (SLMs) in the telecommunications sector, focusing on Phi-2, a compact yet powerful model. Despite the significant interest in Large Language Models (LLMs) for operational efficiency, their substantial size and computational demands pose challenges in resource-constrained environments. Phi-2, a SLM with 2.7 billion parameters, demonstrates comparable performance to larger models like GPT-3.5 and GPT-4 in many tasks, particularly in the telecommunications domain. The study evaluates Phi-2's telecom-specific knowledge using the TeleQnA dataset, which includes multiple-choice questions covering various telecom topics. Phi-2 shows notable strengths in 'Research Overview' and 'Research Publications' but faces challenges in 'Standard Specifications,' a category requiring detailed technical knowledge. To enhance Phi-2's performance, the paper introduces Retrieval-Augmented Generation (RAG), which integrates external knowledge bases to enrich the model's responses. This technique significantly improves Phi-2's accuracy, particularly in the 'Standards Specifications' category, bringing its performance close to that of GPT-3.5. The paper also presents two use cases: network modeling and user association problem solving, demonstrating Phi-2's capabilities and limitations. Overall, the study highlights the potential of RAG in enhancing SLMs for specialized tasks in the telecommunications sector, while also acknowledging the trade-offs between model size, computational efficiency, and task complexity.The paper explores the potential and challenges of Small Language Models (SLMs) in the telecommunications sector, focusing on Phi-2, a compact yet powerful model. Despite the significant interest in Large Language Models (LLMs) for operational efficiency, their substantial size and computational demands pose challenges in resource-constrained environments. Phi-2, a SLM with 2.7 billion parameters, demonstrates comparable performance to larger models like GPT-3.5 and GPT-4 in many tasks, particularly in the telecommunications domain. The study evaluates Phi-2's telecom-specific knowledge using the TeleQnA dataset, which includes multiple-choice questions covering various telecom topics. Phi-2 shows notable strengths in 'Research Overview' and 'Research Publications' but faces challenges in 'Standard Specifications,' a category requiring detailed technical knowledge. To enhance Phi-2's performance, the paper introduces Retrieval-Augmented Generation (RAG), which integrates external knowledge bases to enrich the model's responses. This technique significantly improves Phi-2's accuracy, particularly in the 'Standards Specifications' category, bringing its performance close to that of GPT-3.5. The paper also presents two use cases: network modeling and user association problem solving, demonstrating Phi-2's capabilities and limitations. Overall, the study highlights the potential of RAG in enhancing SLMs for specialized tasks in the telecommunications sector, while also acknowledging the trade-offs between model size, computational efficiency, and task complexity.