Architecture Overview
A production AI app includes frontend, backend API, AI service layer, context management with Redis, and monitoring for logging, usage tracking, and cost analysis.
This separation of concerns makes the system maintainable and scalable.
Building the AI Service Layer
Create a dedicated service for AI interactions that handles prompt construction, context management, retry logic, response parsing, and usage tracking.
This encapsulation makes it easy to swap models or update prompts without changing other code.
Managing Conversation Context
Store conversation history in Redis with time-to-live. Keep the last 10-20 messages for context. Summarize older conversations to save tokens.
Balance between context length for better responses and token usage for cost management.
Streaming for Real-Time UX
Stream responses to the frontend using Server-Sent Events or WebSockets for a real-time typing experience.
Show streaming text with a blinking cursor and allow users to stop generation mid-stream.
Monitoring and Cost Control
Track tokens used per session and user, response latency, error rates, and daily costs. Set up alerts for cost spikes and error rate increases.
Set hard spending limits in your OpenAI account to prevent unexpected bills.