Configure a server-side proxy to handle all AI requests. Never expose your API keys in the browser. Use environment variables to store credentials securely on your backend infrastructure.

Select your preferred LLM endpoint. Map your application requirements to the model capabilities. Ensure your server-side code handles model-specific parameters and response streaming.

Create a secure API route that receives user prompts, attaches your hidden API key, and forwards the request to the AI provider. Return the response stream back to your frontend.

Update your frontend fetch calls to point to your internal API route. Implement loading states and error handling to ensure a smooth user experience during AI response generation.

Implement server-side validation to sanitize user prompts. Prevent malicious injections and ensure that only valid, expected data reaches your AI model endpoint.

Use ReadableStream to handle incoming AI responses. Update your chat interface incrementally as tokens arrive to provide the responsive feel users expect from modern AI apps.

Implement rate limiting on your server-side routes. Prevent abuse and manage your API budget by restricting the number of requests per user or IP address.

Log request latency, error rates, and token usage. Use these metrics to optimize your model selection and identify bottlenecks in your AI integration pipeline.

Run your production build process. Ensure all environment variables are correctly injected into your production environment before deploying to your hosting provider.

Perform a final end-to-end test of your chat interface. Confirm that prompts are routed, responses are streamed, and security measures are functioning as expected.