This work demonstrates the potential of a proposed Large language model (LLM) aided front-end design flow in the early development of verified Register Transfer Level (RTL) design. The proposed framework consists of three task-specific LLMs that generate RTL description, test-bench, and review the design to suggest required modifications depending on the simulation results feedback into the model. The proposed framework has been implemented twice with two different versions of OpenAI LLM viz. GPT-3.5-turbo and GPT-4o-mini. Each implementation has been used independently for developing ten distinct designs of different complexities. The results show that low-complexity designs get generated within a few minutes with fewer feedback or review iterations, moderate complexity designs require more time and iterations as compared to low-complexity designs. However, developing a highly complex design is a bit tricky task. Experimental results show that the proposed framework achieves a higher success rate compared to state-of-the-art methods, automatically fixing various types of bugs when simulation results are fed back into the model. Notably, the framework achieves a 90% success rate with GPT-4o-mini and 84% with GPT-3.5-turbo, demonstrating its robustness across different model configurations.