Production RAG Architecture

Introduction

Shipping RAG to production means thinking about indexing pipelines, freshness, evals, observability, multi-tenancy, security and cost. Here's the full enterprise picture.

Beginner analogy: Going from a hackathon RAG to enterprise RAG is like going from a Django tutorial to running production at scale.

Understanding the topic

Core concepts to understand:

• Ingestion pipeline — connectors, parsers, chunkers, embedders.
• Freshness — incremental re-indexing on document change.
• Multi-tenancy — namespaces or filters per customer.
• Hybrid + re-rank retrieval.
• Evals — separate retrieval and generation metrics.
• Observability — log every query/answer/feedback.
• Cost & cache — embed once, cache LLM completions.
• Security — PII redaction, access control, audit logs.

Syntax reference

Visual workflow / architecture:

Sources (Drive · Notion · DBs)
        │
        ▼
   Ingestion (parse · chunk · embed)
        │
        ▼
   Vector DB + metadata + ACLs
        │
        ▼
   Query → hybrid + re-rank → LLM → answer + citations
        │
        ▼
   Logs · evals · feedback loop

Real-world use

Glean, Notion AI Q&A, Vercel docs assistant, Sourcegraph Cody — production RAG at scale.

Best practices

• Build evals before scaling.
• Always enforce ACLs at query time.
• Cache aggressively to control cost.
• Treat the ingestion pipeline as a first-class system.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Walk through a production RAG architecture.
• Q2. How do you handle multi-tenancy?
• Q3. How do you control cost?