The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating external data through Information Retrieval, thereby enhancing their generation ability. As a recent advancement, RAG improves the selection of knowledge sources for response generation in dialogues. Although LLMs generate answers to questions, these answers may sometimes be of suboptimal quality and contain inaccuracies. The RAG framework includes a fine-tuning process that refines models using feedback and examples based on relevance. This process further enhances Open Domain Question Answering by incorporating external data through Information Retrieval. The RAG end2end extension dynamically updates external data during the training of both the retriever and generator, as well as during the training of Dense Passage Retrieval (DPR) models with QA pairs. This process eliminates the need for large continuous improvements in prediction. RAG goes beyond merely creating a smarter ChatGPT; it enables conversations by integrating external sources, adding personalized external sources, and implementing metrics to evaluate these sources, thereby generating beneficial sources. The framework also employs metrics to evaluate answers, refines them through dialogue and feedback, and reduces hallucinations by augmenting with up-to-date knowledge. In summary, these instances highlight the power of RAG and its potential applications for optimizing language models. However, RAG has some limitations. The quality of the generated responses may be impacted by the quality of the incorporated external data. If the data is inaccurate or biased, this could negatively affect the responses. Furthermore, hallucinations remain a challenge because inaccuracies can arise if the input does not contain sufficient information or metrics for evaluation. Future work should focus on enhancing data integration, educating the prompt query, developing real-time correction mechanisms, and adapting RAG for specific domains.