Often, models are often tested using data splits that include test substrates seen by the model in the training set, resulting in overly optimistic performance metrics. However, when evaluated using a strict test set -where the model must predict yields for entirely new combinations of substrates-the performance typically drops. This strict testing better reflects real-world applications where chemists need to predict reaction yields for novel substrate pairs. Therefore, creating a reaction dataset with a diversified substrate and conditions and implementing rigorous testing protocols and curating relevant datasets is crucial for developing reliable and accurate predictive models. This issue has been widely recognized in the literature, with prior studies highlighting the pitfalls of inadequate dataset partitioning. Recent discussions in the chemical engineering field further emphasize the necessity of rigorous testing protocols to avoid overfitting and ensure model generalizability. The flaws in evaluation methodologies have been acknowledged in various domains, including C-N coupling yield predictions, where flaws in dataset partitioning have been publicly debated and addressed. In this context, we aim to build a high-quality dataset on amide coupling and develop a high-performance yield prediction model that can accurately recommend optimal conditions for novel substrate pairs in the training dataset. In this work, we first demonstrate our efforts to prepare the dataset. We selected substrate pairs according to structures reported in the USPTO reaction dataset and a virtual commercial available space to ensure potential application and structural diversity. Our method employs an unbiased machine-based sampling approach, in contrast to manual selection which can introduce biases, enabling us to cover the chemical space of 70,000 virtual compounds by sampling only 1% of the points. Second, our in-house HTE platform was utilized to collect data, ensuring repeatability. With the dataset in hand, we then focused on developing a robust prediction model. Given the challenge to develop a robust model under 95 conditions, we transformed our goal into an iterative prediction task across the 95 conditions list. Meanwhile, intermediate knowledge was embedded into the model to enhance its performance. The distinguishing feature of this strategy is that the model does not need to learn the relationships among different conditions, while still retaining condition information, thereby providing better performance with high probability. The results of a series of studies revealed that the generalization ability of the model could be significantly improved after applying this strategy (Figure ). The yield prediction error for 18 out of 88 reaction samples, which were from literature entirely unseen in the training dataset, was less than or equal to 5%. Meanwhile, the model could recommend more suitable conditions for some reactions with low yield, indicating the potential application of our work. Additionally, our strategy achieved satisfactory prediction results for reaction pairs with reactivity cliffs, delivering an accuracy of 0.73 in binary classification.