AI Feedback LoopWhat is an AI Feedback Loop?
An AI feedback loop is a continuous improvement cycle where the outputs of an AI model are evaluated and used to refine future predictions. This approach is essential not only to improve model performance over time, but also to detect early signs of model drift which can indicate a degradation in precision, recall, or overall accuracy.
The loop typically involves the model generating results , receiving feedback, either from users or system signals, and then learning from that feedback to enhance its performance. A classic example is the “next best offer” in marketing: the AI suggests a product, the user’s interaction (or lack thereof) is tracked, and that behavior informs future recommendations.
What Are the Challenges?
Capturing meaningful feedback is often easier said than done. In many cases, the only available signals are indirect, such as click-through rates or downstream conversions, which do not always tell the full story. These system-generated metrics can miss nuances like why a user ignored a suggestion or what they expected instead. Human feedback is far richer and can dramatically improve model accuracy, but it is rarely easy to collect.
How Can Write-Back Help?
Write-Back makes it easy to embed feedback collection directly into the Tableau dashboards where users benefit from AI models results. You can configure forms to ask targeted questions about model performance or specific predictions, tailored to each use case. These inputs are stored by Write-Back directly on your database making it easily accessible to any step of the continuous improvement cycle. Because the feedback mechanism is built into the tools users already rely on, participation increases. This transforms feedback from a nice to have into a scalable and actionable input that helps your AI models learn faster and perform better.
Commenting
Forecasting
Managing Reference Data
Mark Annotation
Replacing Values
Root Cause AnalysisUse Cases / Commenting
Commenting
What is Commenting?
Qualitative analysis plays an important part in any analytics process. It is possible to give more context by providing additional information, going beyond what the numbers immediately state. Having specialists commenting on existing analysis and putting it into context for other users means that everyone will get more value from the analysis and be more effective in communication. By establishing a commenting procedure, you ensure more collaboration around the analytics solution and get more value out of it.
Challenges
On Tableau, while we can place comments on a dashboard, these cannot be associated with a particular filter. This means that, for instance, if you place a comment on a particular month, it will be displayed even when you move to the next one. This can sometimes be confusing and does not give you the full picture.
How can Write-Back help you
Write-Back is a great solution for commenting; you can have users providing their input and simply associate it across the board with any filters selected. You are free to choose the form fields and the association with filters meaning it will always be straightforward to fill it in and interpret it. The best thing is that it all gets stored on your database, and you can then use the information in any visualization. Besides this, you can even distinguish who is entitled to comment and users who can only see comments.
Commenting is key to providing more context on complex analyses; with Write-Back, you can take it to the next level and do everything from Tableau dashboards.