Skip to content

Module 15: RAG Architecture at Scale

🚀 Problem Statement

A RAG system that works well with 100 documents might struggle in production with 500K documents across 200 projects. Issues can include hallucinating project-specific details, failing to distinguish between similarly named tasks across different instances, and retrieving irrelevant chunks from unrelated projects.

🧠 The Engineering Story

The Villain: "The Naive Retriever." If all 500K documents are placed in a single vector store, a query about "Task 3 system risks" might retrieve similar-sounding tasks from 15 different projects. The LLM could then conflate them into an incorrect answer.

The Hero: "The Structured RAG Pipeline." Multi-tenancy in retrieval, hierarchical chunking, context-aware prompting, and faithfulness guardrails.

The Plot:

  1. Chunking strategy: Semantic chunking with parent-child relationships (retrieve child, expand to parent for context)
  2. Multi-tenant retrieval: Namespace isolation — each project/document has its own vector partition
  3. Query transformation: Decompose complex queries, generate hypothetical documents (HyDE)
  4. Faithfulness guardrails: Cite sources, detect hallucination, flag low-confidence answers

The Twist (Failure): The Context Window Overflow. If the retrieval step returns 20 highly relevant chunks (e.g., 8K tokens each) and all 160K tokens are stuffed into the prompt, the LLM might ignore middle chunks — a "Lost in the Middle" phenomenon — and answer using only the beginning and end of the provided context.

Interview Signal: Can design a production RAG system with data isolation, quality guardrails, and chunking strategy.

🧠 Production RAG Architecture

graph TD
    Q["User Query"] --> QT["Query Transformer"]
    QT --> |"Decomposed Sub-queries"| R["Hybrid Retriever"]
    R --> |"Per-project namespace"| VS["Vector Store"]
    R --> |"Keyword match"| ES["Search Index"]
    R --> F["Fusion + Filter"]
    F --> RR["Cross-Encoder Re-ranker"]
    RR --> CW["Context Window Manager"]
    CW --> |"Top-K chunks + metadata"| LLM["LLM Generator"]
    LLM --> G["Guardrail Layer"]
    G --> |"Pass"| A["Response + Citations"]
    G --> |"Fail: Low Confidence"| H["Fallback: Human Review"]

🔗 Case Study References