GRAG decouples content grounding and personalization in conversational models by using generic responses from large language models as a structural scaffold. This approach enables smaller, resource-limited models to achieve up to 47% improvement in ROUGE-2 and 36% in BLEU scores over state-of-the-art methods on diverse benchmarks.