Worked case study: HIPAA doc-QA over 50M records

Let's run the whole framework on one prompt: "Design a clinical document Q&A assistant. Staff ask natural-language questions and get cited answers over ~50M patient documents. It must be HIPAA-compliant." This ties every module together.

1. Clarify & assumptions

• Users: clinicians; ~2,000 concurrent, ~50 QPS peak; interactive, target <2s TTFT.
• Task: grounded Q&A with citations over PHI; wrong/unsourced answers are high-cost.
• Data: 50M docs, growing; strict per-patient/role access control; HIPAA (BAA, audit, residency).

2. Success & evals

• Primary: groundedness + citation correctness + retrieval recall@k on a clinician-labeled golden set.
• Guardrails: p95 latency, cost/query, zero cross-patient leakage, refusal-when-unsure rate.

3. Architecture & AI core

AI core = RAG (grounded, citable, fresh) — not fine-tuning. Path: auth'd query → permission-filtered hybrid retrieval → rerank → grounded generation with citations → PHI-safe logging. Offline: ingestion/chunking/embedding with ACLs as metadata.

4. RAG at 50M scale, safely

• ANN (HNSW) index, sharded by facility/tenant, replicated for QPS; metadata pre-filter by patient/role ACL so forbidden docs never enter candidates.
• Hybrid search + cross-encoder rerank; recheck permissions on final chunks before the prompt.
• Incremental indexing for new records; quantization to control memory at 50M vectors.

5. Production: cost, latency, safety, observability

• Latency: stream tokens; cap output; tight retrieved-context budget for low TTFT.
• Cost: route easy queries to a small model, escalate hard ones; prefix-cache the stable system prompt; tenant-scoped semantic cache.
• Safety/PHI: retrieval-time ACLs, redact PII in logs, VPC deployment + BAA, audit log every access, treat retrieved notes as data (injection defense).
• Reliability: timeouts, backoff retries, provider fallback, graceful "insufficient evidence" answer over guessing.
• Observability: full tracing, offline evals gating deploys, online groundedness sampling + clinician thumbs feeding the feedback loop.

6. Tradeoffs (the close)

"I'm optimizing for groundedness and zero leakage over raw latency — reranking and permission rechecks add ~100-200ms I'm willing to pay in a clinical setting. If usage or cost spikes, I'd lean harder on caching and routing. I'd trust this once the golden-set groundedness and the leakage test both pass in CI."