Why AI Integration Matters
AI is no longer a buzzword — it's a practical tool that can dramatically improve user experiences and business operations. But integrating AI into existing applications requires careful architecture decisions.
Pattern 1: AI as a Service
The simplest pattern is treating AI as an external service:
class AIService {
async summarize(text: string): Promise<string> {
const response = await openai.chat.completions.create({
model: "gpt-4",
messages: [
{ role: "system", content: "Summarize the following text concisely." },
{ role: "user", content: text },
],
});
return response.choices[0].message.content ?? "";
}
}
Pattern 2: Queue-Based Processing
For non-real-time AI tasks, use background processing:
class ProcessDocumentJob implements ShouldQueue
{
public function handle(AIService $ai): void
{
$summary = $ai->summarize($this->document->content);
$this->document->update(['summary' => $summary]);
}
}
Pattern 3: Streaming Responses
For chat-like interfaces, stream AI responses:
async function* streamResponse(prompt: string) {
const stream = await openai.chat.completions.create({
model: "gpt-4",
messages: [{ role: "user", content: prompt }],
stream: true,
});
for await (const chunk of stream) {
yield chunk.choices[0]?.delta?.content ?? "";
}
}
Error Handling & Fallbacks
AI services can fail. Always have a fallback:
- Implement circuit breakers for external AI APIs
- Cache frequently requested AI responses
- Provide graceful degradation when AI is unavailable
- Set reasonable timeouts for AI operations
Cost Management
AI API calls cost money. Control costs by:
- Caching identical requests
- Using smaller models for simple tasks
- Batching requests where possible
- Setting per-user rate limits
Conclusion
Start with the simplest pattern that solves your problem. You can always add complexity later. The key is making AI integration transparent and resilient.