The rapid progress of generative AI has advanced natural language processing, yet large language models (LLMs) often underperform on domain-specific tasks and struggle to align with human preferences. While Retrieval-Augmented Generation (RAG) and fine-tuning offer solutions, they face challenges such as noisy retrievals, static knowledge reliance, and catastrophic forgetting. This paper presents FineRAG, a novel framework that combines instruction tuning with an optimized RAG mechanism to enhance domain-specific performance in LLMs. FineRAG utilizes the InstructLab for high-quality data curation and integrates advanced retrieval techniques to reduce noise, improve relevance, and ensure real-time scalability. By dynamically combining retrieval and fine-tuning, FineRAG delivers contextually aligned, high-quality responses while mitigating common issues like hallucinations. Experimental evaluations highlight FineRAG’s superiority over naive RAG systems, demonstrating improved performance across metrics such as faithfulness, context relevancy, and semantic similarity. FineRAG achieves greater accuracy and reliability through refined query handling, context rephrasing, and efficient real-time retrieval. These findings establish FineRAG as a robust and scalable framework, providing a versatile solution for domain-specific NLP tasks. FineRAG sets a new standard for adapting LLMs to real-world applications by addressing critical challenges in retrieval and generation.