Best Practices for Production AI Applications

# Best Practices for Production AI Applications Deploying AI applications to production requires careful consideration of performance, reliability, and user experience. Here are our battle-tested best practices. ## 1. Streaming for Better UX Always use streaming for long-running AI responses: ```typescript const stream = await client.chat.completions.create({ model: 'gpt-4', messages: messages, stream: true }); for await (const chunk of stream) { process.stdout.write(chunk.choices[0]?.delta?.content || ''); } ``` ## 2. Error Handling Implement comprehensive error handling: ```typescript try { const response = await client.chat.completions.create(request); } catch (error) { if (error.code === 'rate_limit_exceeded') { // Implement exponential backoff await retry(request, { maxAttempts: 3 }); } else if (error.code === 'context_length_exceeded') { // Truncate or summarize context request.messages = truncateMessages(request.messages); } else { // Log and notify logger.error('AI request failed', error); } } ``` ## 3. Cost Optimization - **Caching**: Cache common responses - **Model Selection**: Use smaller models when appropriate - **Prompt Engineering**: Optimize prompts for clarity and brevity - **Batch Processing**: Process multiple requests together ## 4. Security - **Input Validation**: Sanitize all user inputs - **Rate Limiting**: Prevent abuse - **API Key Management**: Use environment variables - **Output Filtering**: Monitor and filter AI responses ## 5. Monitoring Track key metrics: - Response time - Token usage - Error rates - User satisfaction - Cost per request ## 6. Testing - **Unit Tests**: Test individual components - **Integration Tests**: Test AI workflows - **Load Tests**: Verify performance at scale - **A/B Tests**: Compare model performance Build reliable AI applications with these proven practices!

Best Practices for Production AI Applications

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