This paper explores the evolution of large language models (LLMs) and the growing role of retrieval-augmented generation (RAG) systems in overcoming challenges in domain-specific applications. Although LLMs have revolutionized natural language processing (NLP), they face critical limitations in high-stakes domains such as medicine, engineering, and law where accuracy, factuality, and trust are paramount. These shortcomings include hallucinations, outdated knowledge, and vulnerability to adversarial prompts. RAG systems address these issues by integrating LLMs with external domain-specific knowledge sources to improve factual grounding and response reliability. Frameworks like Almanac in clinical settings and KEAG in complex QA tasks demonstrate how RAG reduces hallucinations, enhances interpretability, and delivers accurate, evidence-backed responses. In healthcare, combining LLMs with RAG has raised accuracy from around 93.25% up to 99.25%, showing its impact on real-world decision support. This paper proposes a structured synthesis of advancements, challenges, and optimization strategies in RAG for specialized domains, paving the way for safer, transparent, and adaptive AI systems.