DEEP EXPLANATION

Design a RAG pipeline for enterprise documents (EXPLAINED)

Company BasedRAGHard25 min read

Enterprise RAG interviews test system design at scale: ACL-aware retrieval, audit logging, and ingestion pipelines for millions of documents. This is a staff-level question appearing at Microsoft, Salesforce, and Fortune 500 AI teams. Walk through a complete architecture with security boundaries and operational concerns.

RAG · System Design

TL;DR — Quick Answer

Ingestion pipeline with OCR, ACL-aware chunking, vector store with namespace isolation, query-time permission filtering, and full audit trail.

The Interview Question

Design an end-to-end RAG system for 10M enterprise PDFs with access control and audit logging.

Deep Explanation

Architecture: Document ingestion service → OCR/parsing → chunking with metadata (doc ID, section, ACL groups) → embedding batch jobs → vector DB with per-tenant namespaces.

Query path: Auth middleware → embed query → filter by user permissions → retrieve → re-rank → generate with citations → log query/response/retrieved docs for audit.

Enterprise RAG with ACL

Permission-aware ingestion and query-time filtering

Consider: incremental indexing, deletion propagation, PII redaction, and SLA for query latency.

Real-World Examples

SharePoint connector with group-based ACLs

Common Mistakes

Ignoring permission filtering at retrieval time
No strategy for document updates/deletions

What Interviewers Expect

✓Security and compliance awareness
✓Scalable ingestion design
✓Operational concerns (monitoring, SLAs)

Follow-Up Questions

How do you handle document versioning?
What vector DB would you choose and why?

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