Data and Evaluation Closed-Loop for Model Capability Enhancement

1. 1Build a data pipeline to collect and process samples, prompts, and decoding rules
2. 2Run evaluations to assess model capability and identify areas for improvement
3. 3Configure the model to incorporate feedback from the evaluation loop
4. 4Test the updated model using a closed-loop data and evaluation process
5. 5Apply optimization techniques to refine the model capability

Machine learning engineers and data scientists can benefit from this process to improve model performance and address optimization challenges, while working together to refine the model capability

💡 Closed-loop data and evaluation enables prospective shaping of model capability and retrospective revelation of its strengths and weaknesses

🚀 Enhance model capability with closed-loop data & evaluation! 📊