Problems / RAG and retrieval, medium
Corrective RAG grades retrieved chunks with an evaluator model before answering. The evaluator has 20 slots and grades every question.
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Success ≥ 99%, p99 ≤ 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 5; 1 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 Hybrid search, Retrieval evaluator, GPU serving pool.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| Hybrid searchHybrid search | AI app calls it.Hybrid search combines keyword matching (BM25) with vector similarity. | 400 slots · 2 steps |
| Retrieval evaluatorLLM-as-judge | AI app calls it.An LLM judge grades outputs at scale, but it is another model call with its own latency, cost and biases. | 20 slots |
| GPU serving poolSelf-hosted serving pool | AI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out. | 400 slots |