Problems / AI products, medium
A customer-support bot with a semantic cache, retrieval and a hosted model launches to 60 questions a second. The token quota wasn’t raised.
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Success ≥ 99%, time to first token p99 ≤ 2.5 s.
Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 6; 2 of them are hidden until then.
60 requests a second for 30 seconds: 100% request (reads).
What each component does in this design, and what calls it. The simulator mimics each one from these settings.
| Component | Its job here | Settings |
|---|---|---|
| Load balancerLoad balancer | Traffic enters here.Calls AI app.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header. | – |
| AI appApp service | Load balancer forwards to it.Calls Semantic cache (checked first), Help-centre search, Model.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| Help-centre searchHybrid search | AI app calls it.Hybrid search combines keyword matching (BM25) with vector similarity. | 400 slots · 2 steps |
| ModelLLM endpoint | AI app calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second. | 400 streams · 5M input tokens/min |
| Semantic cacheSemantic cache | AI app checks it first.A semantic cache matches questions by meaning using embeddings, not exact text. | 15% hit rate |