The proposed reward engineering approach can be extended to handle more complex and diverse types of queries by incorporating a more sophisticated feature set that captures the nuances of different query types. For instance, the reward function could include features related to the complexity of the query, the specificity of the information requested, the context of the query, and the desired tone or style of the response. By designing a reward function that considers a broader range of query characteristics, the model can learn to generate responses that are tailored to the specific requirements of each query type. Additionally, incorporating multi-task learning techniques could enable the model to simultaneously optimize for various aspects of the response, such as relevance, coherence, and informativeness, across different query types.