Potential risks associated with RAG implementation include the propagation of biases in the training data and the possibility of generating irrelevant information. These risks can be mitigated by employing diverse training datasets, regularly updating the model with accurate information, and incorporating robust validation mechanisms to continually improve the model's outputs.