3. RAG ENGINEERING MODULE

(Welcome to the practical section. Everything before this was foundation. Everything here is production-grade engineering. The examples look simple - they're not. Each design decision has failure modes that won't appear until you hit production traffic. We'll point them out as we go.)

What is RAG (Retrieval-Augmented Generation)?

Problem RAG Solves:

• LLMs have knowledge cutoff dates (trained on old data)
• LLMs hallucinate (make up facts confidently)
• LLMs can't access private/proprietary data
• LLMs have token limits (can't process entire databases)

Solution: Retrieve relevant information → Feed to LLM → Generate grounded answers

RAG Pipeline (Basic)

User Query
    ↓
[1] Query Processing (rewrite, expand)
    ↓
[2] Retrieval (search documents)
    ↓
[3] Context Construction (format retrieved docs)
    ↓
[4] LLM Generation (answer with context)
    ↓
Answer

Concrete Example

Without RAG:

User: "What was our Q4 2024 revenue?"
LLM: "I don't have access to real-time data..."

With RAG:

User: "What was our Q4 2024 revenue?"
    ↓
Retrieval: Find "Q4_2024_earnings.pdf"
    ↓
Context: "Q4 2024 revenue: $5.2M, up 23% YoY..."
    ↓
LLM: "According to the Q4 2024 earnings report, revenue was $5.2M, representing a 23% increase year-over-year."