Google AI Studio has introduced new tools designed to help developers explore, debug, and share model interaction logs, turning user insights into tangible improvements for product quality and model performance. Every interaction with a model presents an opportunity to refine and enhance the user experience, and these new capabilities make it easier to extract meaningful data from real-world usage. Users can now export logs as structured datasets in CSV or JSONL formats, enabling offline analysis and testing. This allows teams to identify specific instances where model responses were subpar or exceptionally strong, helping to establish a reliable and reproducible baseline for expected performance. These datasets can be used for prompt optimization, tracking model improvements over time, and validating changes before they go live. With the Gemini Batch API, teams can run large-scale evaluations on these custom datasets, enabling systematic testing of model updates, changes in model selection, or modifications to application logic. This helps ensure that updates deliver consistent results before being rolled out to end users. The Datasets Cookbook offers practical examples and guidance for building and using these datasets effectively. Additionally, users have the option to share anonymized datasets with Google to provide feedback on end-to-end model behavior in real-world scenarios. These contributions help Google refine and enhance its AI products and services, including improving model training and overall system performance. By participating, developers not only strengthen their own applications but also contribute to the broader advancement of AI technology.