Generate Dynamic Frontend Components with Gemini 3.5 Flash

Create a schema that captures the 'Intent' of the UI. Instead of static props, define the data to be displayed and the user's current goal (e.g., 'Visualize quarterly churn'). This intent serves as the context for Gemini's design decisions.

{
  "data": [{"month": "Jan", "churn": 5}, ...],
  "intent": "High-level executive summary with trend analysis",
  "theme": "corporate-dark"
}

Watch out: Ensure the data sent to the AI is anonymized. Never send raw PII (Personally Identifiable Information) in the component intent schema.

Use a specialized prompt that instructs Gemini to return a functional React component using Tailwind CSS. The prompt must include a 'System Instruction' that enforces accessibility standards and your project's specific design tokens.

component_code = gemini.generate("Create a React component for intent: {{intent}}")

Watch out: Use the json_mode or strict formatting to ensure the AI returns ONLY the component code, free of markdown artifacts or conversational text.

Because the AI-generated code will be executed in the user's browser, you must sanitize it to prevent XSS (Cross-Site Scripting). Pass the code through a secure transpiler like Babel (running in a worker) to convert JSX to standard JavaScript.

const safeCode = transform(generatedCode, { presets: ['react'] }).code;

Watch out: Never use a raw eval() on AI-generated strings. Use a secure iframe sandbox or a specialized runtime library like react-runner.

Load the sanitized code into a dynamic component wrapper. Wrap this component in a React Error Boundary to catch any runtime errors (e.g., the AI used a non-existent prop) and fall back to a standard 'Loading' or 'Error' state.

<ErrorBoundary fallback={<DefaultUI />}>
  <DynamicComponent code={safeCode} />
</ErrorBoundary>

Watch out: AI-generated components may occasionally trigger 'infinite re-renders' if they handle state poorly. Set a strict execution timeout in your sandbox.

Track how users interact with the generated component. If a user closes the component immediately or fails to interact with it, log this as a 'Negative Intent Match' and feed the feedback back into the prompt to improve future generation quality.

Watch out: Keep the feedback loop automated. Use a small scoring model to weigh user interaction data before adjusting the primary generation prompt.