Module 9: Production RAG Architecture Slides

Slide Outline

1. Production RAG Architecture - Scaling, multi-tenancy, caching, API gateways, and high-availability RAG deployments
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - Building a RAG prototype takes an afternoon. Running it in production for 10,000 users takes engineering. This module te
4. Scaling RAG Systems - Lesson section from the full module
5. Multi-Tenant RAG - Lesson section from the full module
6. Caching Strategies - Lesson section from the full module
7. API Design - Lesson section from the full module
8. Common Mistakes to Avoid - 4 mistakes covered
9. Hands-On Labs - 2 hands-on labs
10. Key Takeaways - 5 points to remember

Learning Objectives

• Design scalable RAG architectures for production traffic
• Implement multi-tenant retrieval with data isolation
• Add caching layers for cost and latency optimization
• Build production RAG APIs with FastAPI

Why This Module Matters

Building a RAG prototype takes an afternoon. Running it in production for 10,000 users takes engineering. This module teaches the architecture patterns that separate demo projects from production systems.

Common Mistakes

• No caching — every identical question re-embeds, re-retrieves, re-generates
• No rate limiting — one heavy user exhausts your LLM API budget
• Shared collections without tenant filtering — data leaks between customers
• Synchronous responses — users wait 3-10 seconds without streaming feedback

Key Takeaways

• Production RAG needs caching, auth, rate limiting, streaming, and monitoring
• Semantic caching reduces LLM costs by 40-60% for repeated queries
• Multi-tenant isolation via metadata filtering is efficient but requires careful access control
• Streaming responses improve perceived latency for users
• Monitor cost per request — LLM API calls are the largest expense

Hands-On Labs

1. Deploy Scalable RAG API

   Build a production-ready RAG API with FastAPI.

   40 min - Advanced

   • Build FastAPI RAG endpoint with streaming
   • Add Redis caching for repeated queries
   • Implement API key authentication
   • Load test with locust and measure throughput

   View lab files on GitHub

2. Implement Multi-Tenant RAG

   Isolate data between tenants in a shared RAG system.

   30 min - Advanced

   • Create Qdrant collections with tenant metadata
   • Filter retrieval by tenant_id
   • Test data isolation between tenants
   • Measure performance impact of filtering

   View lab files on GitHub