Large Language Models (LLMs) are impressive, but they sometimes struggle with accessing the most up-to-date information, leading to inaccuracies or 'hallucinations.' Retrieval Augmented Generation (RAG) helps by pulling knowledge from external databases, but simply grabbing the top-ranked chunks isn't always effective. A new system called CORAG tackles this by strategically selecting the *best combination* of information chunks, considering their relationships and relevance to the specific query. Imagine trying to find information about a historical figure: instead of just retrieving individual facts, CORAG assembles a cohesive narrative by combining relevant chunks in the optimal order, similar to piecing together a puzzle. This method uses a 'policy tree' to explore different combinations, guided by an intelligent agent that dynamically adjusts retrieval strategies based on the query type. Experiments show CORAG boosts accuracy by up to 30% compared to standard methods, while also being efficient and scalable for large datasets. This smarter retrieval approach paves the way for more accurate and reliable LLM applications by ensuring the model has the best possible context for generating its responses.