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In this webinar, Prabhavathi Kannan demonstrated how to enhance the Syncfusion® JavaScript DataGrid with Azure OpenAI to create an AI-powered, predictive data-entry experience. The session showed how entire datasets can be sent to an AI model to predict values, calculate totals, assign grades, and update the grid dynamically without writing traditional calculation formulas.
If you missed the webinar or would like to review part of it, the recording has been uploaded to our YouTube channel and embedded below.
Time stamps
- [00:00] Welcome and session introduction.
- [00:46] Agenda and session goals.
- [01:11] Poll: What would you want AI to predict?
- [01:48] Overview of the AI-driven DataGrid concept.
- [02:41] Syncfusion AI-ready toolkit overview.
- [03:33] Prerequisites and setup requirements.
- [03:57] Poll: Most-used Syncfusion control.
- [04:27] Project setup in Visual Studio Code.
- [05:39] Understanding the dataset and grid structure.
- [06:31] Initial grid configuration and layout.
- [07:29] Creating the grid and toolbar button.
- [10:11] Wiring the Calculate Grade button.
- [14:10] Running the base grid.
- [14:41] Adding Azure OpenAI integration.
- [17:21] Generating prompts for AI predictions.
- [18:50] Executing AI logic and grading rules.
- [20:04] Updating the grid with predicted values.
- [21:56] Styling cells and adding animations.
- [23:46] Live demo: AI-powered grading in action.
- [24:54] Key takeaways and recap.
- [25:21] Applying this approach to real-world apps.
What was built in this session
The demo application used the JavaScript DataGrid populated with student GPA data from three academic years. With a single button click, the dataset was sent to Azure OpenAI, which predicted the final-year GPA, calculated the total GPA, and assigned letter grades automatically.
Syncfusion AI-ready toolkit overview
Syncfusion’s AI-ready toolkit enables seamless integration with AI models such as Azure OpenAI, OpenAI, Gemini, and Anthropic. The toolkit has components for major frameworks, including JavaScript, React, Angular, Vue, Blazor, and ASP.NET.
Prerequisites and project setup
To follow along, developers need Visual Studio Code, Node.js, TypeScript, access to Azure OpenAI with a deployed model, and the Syncfusion JavaScript DataGrid packages.
How the AI integration works
The solution uses a structured prompt that combines grid data and grading rules into a single instruction. Azure OpenAI returns a JSON-only response, which is parsed and bound directly back to the DataGrid for predictable results.
Dynamic updates, styling, and animations
Predicted values are applied row by row with smooth animations. Custom cell styling uses color coding to highlight performance, providing immediate visual clarity.
Q&A
Q: Can we use it on projects for other companies?
A: Yes, you can use it on other companies’ projects. The key requirement is to generate an appropriate prompt for the AI based on the specific needs and component settings. Once the AI provides its response, you will need to programmatically update the component to reflect the changes and display them in the UI.
Q: How does this thing internally work?
A: First, determine whether the desired functionality is achievable within the component. If it is, generate a tailored prompt to obtain a suitable response from the AI that aligns with the component’s capabilities. Based on the AI’s output, execute the corresponding actions or updates within the component.
Q: Can you create a prompt that will allow AI to dynamically answer the question and adjust the grid to respond to what was entered into the prompt?
A: Yes, this is achievable. For an example of an interactive grid powered by AI, please refer to this Syncfusion JS 2 React demo.
Q: If a user enters a question, will the grid adjust, or does the grid need to be preprogrammed ahead of time?
A: Yes, dynamic adjustment is possible without preprogramming every possible query. The grid can respond intelligently to natural language inputs in real time. Please refer to this interactive AI grid demo for a practical illustration.
Takeaways
This approach eliminates manual calculations, simplifies business logic, and enables developers to build intelligent, AI-driven data grids applicable to many real-world scenarios beyond grading.
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