To ensure the quality and diversity of the calibration questions for accurate representation of real-world usage in the RAG framework, several strategies can be implemented. Firstly, it is essential to involve domain experts or individuals representative of the target users in crafting the questions. This ensures that the questions are relevant, comprehensive, and aligned with the actual queries that the RAG framework is expected to handle. Additionally, incorporating a wide range of topics, complexities, and scenarios in the calibration questions can enhance diversity and coverage.
Moreover, leveraging existing datasets or generating questions from a diverse set of reference documents can help in creating a robust calibration set. Using techniques like active learning to iteratively improve the quality of questions based on feedback from the retrieval process can also enhance the calibration set. Regularly updating and expanding the calibration dataset to reflect evolving user needs and knowledge base updates is crucial for maintaining relevance and accuracy in real-world applications of the RAG framework.