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The Core RAG Pattern

RAG isn’t just “search + LLM.” It’s a carefully designed pipeline with specific stages, each solving a distinct problem. 
+------------------+
│    User Query    │
+------------------+
        |
        v
+------------------+
│  Stage 1:        │
│  Fast Retrieval  │  ← Get 10-50 candidates quickly
│  (Lexical/Vector)│     (prioritize recall over precision)
+------------------+
        |
        v
+------------------+
│  Stage 2:        │
│  Reranking       │  ← Narrow to top 3-5 precisely
│  (Cross-Encoder) │     (prioritize precision over speed)
+------------------+
        |
        v
+------------------+
│  Stage 3:        │
│  LLM Generation  │  ← Use retrieved context to generate
│  (GPT-4/Claude)  │     grounded response
+------------------+
        |
        v
+------------------+
│    Response      │
+------------------+

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Why Two-Stage Retrieval?

The Fundamental Trade-off:
  • Fast retrieval methods (lexical, vector) can process 100K+ documents in milliseconds
  • Accurate ranking methods (cross-encoders) can only handle ~100 documents in reasonable time
  • Solution: Use fast method to filter, accurate method to rank
Latency in practice (varies by system):
  • First-pass retrieval returns a small candidate set quickly (implementation-, scale-, and hardware-dependent).
  • Cross-encoder reranking narrows to top results but adds additional latency.
  • Production systems typically target interactive end-to-end latency budgets on available hardware.

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Basic RAG Implementation

Let’s start with the simplest working version: Full runnable example of a simple RAG What Makes This Production-Ready? Not much yet! This prototype has several problems:
  • ❌ No error handling
  • ❌ No caching (repeated queries waste $$$)
  • ❌ No retrieval quality measurement
  • ❌ Single-stage retrieval (accuracy suffers)
  • ❌ No lexical/vector hybrid search
  • ❌ No metadata or filtering
We’ll fix these throughout the module.

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Production RAG: Key Components

A production RAG system needs:
  1. Document Processing Pipeline
    • Chunking strategy (size, overlap)
    • Metadata extraction (title, date, source)
    • Quality filtering
  2. Two-Stage Retrieval
    • First-pass: Fast, broad recall (lexical or vector)
    • Reranking: Slow, precise scoring
  3. Context Engineering
    • Prompt design for grounding
    • Citation formatting
    • Handling insufficient context
  4. Evaluation Framework
    • Retrieval metrics (Recall@k, NDCG)
    • Generation metrics (faithfulness, relevance)
    • Component-level debugging
  5. Observability
    • Retrieval quality monitoring
    • Latency tracking
    • Cost per query
We’ll build each component step by step.