Enterprise Production RAG
Advanced⏱ 3–4 weeks · 7 milestones
Build a production-grade RAG system with multi-tenancy, reranking, hybrid search, streaming responses, observability, and LLM gateway rate limiting.
Difficulty
Advanced
Duration
3–4 weeks
Milestones
7 steps
Interview Qs
4 questions
🛠 Tech stack
✅ Prerequisites
- ● RAG fundamentals (built a basic RAG)
- ● MLOps
- ● System design
- ● Advanced Python
Step-by-step milestones
Hybrid search pipeline
Combine BM25 sparse + dense vector retrieval.
Reranking
Add cross-encoder reranking for top-K results.
Multi-tenancy
Isolate data per user/org with namespace partitions.
Streaming responses
Stream tokens to the UI via SSE.
Observability
Trace every query with latency, token cost, and retrieval quality.
LLM gateway
Add rate limiting, fallbacks, and cost tracking.
Load testing
Benchmark at 100 concurrent users.
Skills you'll build
📝 Interview questions you'll face
- 1.How does hybrid search improve recall?
- 2.How do you handle reranking latency?
- 3.How do you debug a RAG system that gives wrong answers?
- 4.Design a RAG system for 10M documents.
🌟 Portfolio guidance
Document architecture decisions with diagrams. Include benchmark numbers (latency P50/P99, retrieval precision). This alone can get you senior-level interviews.