From Chat to Action – Empowering Users to Transform Data Grids with Natural Language

The integration of LLMs into web applications has largely focused on content generation and chatbots. But for enterprise developers, the “Holy Grail” is functionality: How do we turn a user’s natural language request into a specific UI action?

We are excited to introduce a New AI Example in the Sencha ecosystem designed to solve exactly this problem. This example simplifies the integration of LLMs with Ext JS applications, demonstrating how to generate structured outputs that enable end users to manipulate the state of an Ext JS Grid using only natural language.

The Challenge – Bridging Natural Language and Structured Data

Enterprise applications are built on structured data displayed in grids, tables, and charts. Yet users think and communicate in natural language. This disconnect has historically forced users to learn complex interfaces, memorize filter syntax, and navigate through multiple menus to get the insights they need.

The challenge for developers has been equally daunting: How do you translate a user’s natural request into precise grid operations? How do you ensure the LLM understands your data schema? And most importantly, how do you make this integration reliable enough for production use?

The Solution: Structured Output Generation with LLM Integration

Our new example demonstrates a pattern for forcing LLMs to bypass conversational fluff and return structured outputs. By defining a strict schema that the AI must follow, we can translate human intent directly into Ext JS Grid state configurations.

How It Works

1. Natural Language Input: The user types a query into the application (e.g., “Filter by Sales Department and sort by Revenue descending”).
   
2. AI Processing: The LLM analyzes the intent against the known columns and capabilities of your Grid.
3. Structured Output: Instead of responding with a chat, the LLM generates a JSON object representing the new Grid State.
4. State Manipulation: The application applies this state to the Grid, instantly updating filters, sorters, and visibility without the user ever touching a column header.

Implementation Benefits

For Developers

• Reduced Complexity: No need to build complex query builders or filter interfaces
• Maintainable Code: Structured outputs mean predictable, testable integrations
• Framework Agnostic: The pattern works with any LLM provider

For End Users

• Zero Learning Curve: Users interact using their natural vocabulary
• Faster Insights: No clicking through menus—just ask for what you need
• Error Tolerance: LLMs can interpret intent even with typos or ambiguous requests
• Accessibility: Natural language interfaces are inherently more accessible

Security and Control

While powerful, this approach maintains enterprise-grade security:

• All operations are validated against user permissions
• The LLM never directly accesses your database
• Structured outputs prevent injection attacks
• Audit logs track all natural language requests and their translations

Try It Today

The AI-powered grid example is available now as part of Sencha’s growing collection of AI integration patterns. Whether you’re building a new application or enhancing an existing one, this pattern can dramatically improve how your users interact with data.

Visit our Resource Center to access the complete example code, documentation, and integration guides. Join us in pioneering the next generation of intuitive, AI-enhanced user interfaces.