Imagine asking an AI a complex question, and instead of vague or inaccurate answers, it delivers precisely what you need, grounded in verifiable facts. That's the promise of Retrieval Augmented Generation (RAG), a technique that allows AI to access and process external information to answer queries. However, current RAG systems face a challenge: their accuracy can decrease as the amount of data increases. Even with smaller datasets, they sometimes miss the mark on simple questions. Why? Because they rely on large language models (LLMs) which, despite their power, can be probabilistic and uncertain. This new research introduces a clever solution: *semantic tokens*. Think of them as special tags that link pieces of retrieved information to external, verifiable data. By incorporating an 'evaluator module,' this new Comparative RAG system adds a layer of deterministic reasoning to the probabilistic nature of LLMs. This evaluator compares what the AI retrieves with external data, ensuring that the AI's answers are not only relevant but also *accurate*. This is particularly important in areas requiring high precision, like medical diagnosis or legal research. In a practical example, imagine a food delivery app. This new system could use external metrics like customer reviews and delivery times to create a 'desirability index' for restaurants. Then, when a user searches for 'best Italian food nearby,' the AI can use the semantic tokens to prioritize restaurants based not just on keywords, but also on this verifiable desirability index. This research represents a significant step towards more reliable and scalable question-answering systems. By combining the semantic power of LLMs with the precision of deterministic verification, we can unlock the true potential of AI to provide accurate, grounded, and truly helpful information.