This thesis systematically compares Retrieval-Augmented Generation (RAG) and fine-tuning (FT) adaptation strategies for large language models (LLMs) in conversational AI, emphasizing practical enterprise deployment. Four strategies were benchmarked under reproducible conditions on the public OpenAssistant/oasst1 dataset: RAG implemented on Llama2-7B and Gemini 2.0 Flash, and fine-tuning of both models. Model performance was rigorously assessed using automated metrics (BLEU, ROUGE, METEOR, BERTScore, semantic similarity) alongside structured human evaluations conducted in collaboration with Sinch AB. Fine-tuning methods, particularly for Llama2-7B, demonstrated superior alignment with ground-truth responses and consistent brand-aligned tone, ideal for public-facing chatbots. Conversely, RAG excelled in factual accuracy and completeness, especially in dynamic domains. The results highlight complementary strengths, underscoring the importance of combined automated and human evaluation. Practically, the thesis provides actionable insights: RAG suits internal or rapidly evolving contexts, while fine-tuning excels for external communication with strict brand requirements. Future research should investigate hybrid approaches, integrating the best features of both strategies. This thesis thus bridges academic and practical deployment gaps, providing enterprise stakeholders clear guidance on adapting LLMs effectively.