Retrieval Augmented Generation (RAG) techniques aim to improve the factual accuracy of text generated by models by incorporating context from a knowledge base. Corrective RAG, specifically, addresses inaccuracies by ranking options based on how well they align with the model and retrieved information, ensuring accurate corrections in real-time. The Corrective Retrieval Augmented Generation (CRAG) framework comprises a Generative Model, a Retrieval Model, and a Retrieval Evaluator, which collaboratively generate, retrieve, and evaluate information to produce accurate outputs. Implementation involves using tools like Langgraph and LanceDB for document retrieval and OpenAI embeddings for context extraction. The framework faces challenges such as dependency on comprehensive knowledge bases, increased computational costs, and balancing fluency with factuality. Despite these challenges, CRAG represents a significant step towards more reliable text generation by integrating corrective mechanisms into the generative process.