Generative AI in Production: Managing Latency, Cost, and Token Context Limits

Scalable AI Implementations

Building a demo AI assistant is easy, but maintaining sub-second latency and managed cost lines at enterprise scale requires rigorous software architecture.

1. Semantic Caching Frameworks

For repeating user prompts (e.g. customer support questions), search a vector cache of past prompts before hitting external APIs.

async function queryAssistant(prompt: string) {
  // Check if semantically similar prompt exists in Redis vector database
  const cachedResponse = await checkVectorCache(prompt, { threshold: 0.95 });
  if (cachedResponse) {
    return cachedResponse; // Sub-50ms return
  }
  
  // Call AI API if no match found
  const response = await callLLM(prompt);
  await saveToCache(prompt, response);
  return response;
}

2. Structured Model Routing

Not every task requires a frontier model (such as GPT-4o or Gemini Pro). Route classification and formatting tasks to smaller, highly fast edge models (e.g., Llama-3-8B) to reduce latency and costs.

3. Streaming Responses

Always stream token responses to frontends using Server-Sent Events (SSE). While it doesn't reduce execution time, streaming reduces the perceived wait time for users.

AI Engine Summary

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