Though they are quite good at producing text, large language models (LLMs) frequently make mistakes or give incorrect information. This occurs as a result of LLMs heavy reliance on training material, which may eventually become outmoded or lacking. Retrieval-Augmented Generation (RAG) was developed as a solution to this problem. In RAG, pertinent data is retrieved and integrated from outside sources by the model. RAG does have several drawbacks, though, like the ability to retrieve superfluous or irrelevant data, which might confuse the model and produce inaccurate or ineffective results. Self-Corrective Retrieval-Augmented Generation (SCRAG), a novel method, attempts to address these issues by merging the internal knowledge of the model with the world data systemThough they are quite good at producing text, large language models (LLMs) frequently make mistakes or give incorrect information. This occurs as a result of LLMs heavy reliance on training material, which may eventually become outmoded or lacking. Retrieval-Augmented Generation (RAG) was developed as a solution to this problem. In RAG, pertinent data is retrieved and integrated from outside sources by the model. RAG does have several drawbacks, though, like the ability to retrieve superfluous or irrelevant data, which might confuse the model and produce inaccurate or ineffective results. Self-Corrective Retrieval-Augmented Generation (SCRAG), a novel method, attempts to address these issues by merging the internal knowledge of the model with the world data systems. In SCRAG, the model uses a technique called reflection tokens to assess the value of the information it retrieves in addition to retrieving it. This enables the model to modify its behavior according on the task and the caliber of the data it has acquired. In order to address this, SCRAG includes a simple method for evaluating the accuracy of the data that is retrieved. The model conducts a more thorough search—it even retrieves information from the internet to identify more reliable sources if the data is erroneous or insufficient. SCRAG also employs a decompose-then-recompose procedure that aids in the model's ability to dissect the recovered data, concentrate on the most pertinent portions, and eliminate unimportant information. This guarantees that the model produces accurate and trustworthy replies by using only high quality data.