Retrieval-Augmented Generation (RAG): A Practical Guide for Data Engineers

Large Language Models (LLMs) are powerful, but they have a serious limitation:
they don’t know your data.

Retrieval-Augmented Generation (RAG) is the most common way to fix that — and it’s much more of a data engineering problem than an ML one.

This article explains: - What RAG really is (without buzzwords) - How a production-ready RAG pipeline works - Where data engineers add the most value - Common mistakes teams make when building RAG systems

What Is RAG (In Simple Terms)

RAG is a pattern where an LLM: 1. Retrieves relevant data from an external system 2. Uses that data as context to generate an answer

Instead of asking:

“LLM, answer based on your training data”

You ask:

“LLM, answer using these documents I just retrieved”

The LLM does not search your data itself.
Your system does.

High-Level RAG Architecture

A typical RAG system looks like this:

Data Sources
   ↓
Ingestion & Cleaning
   ↓
Chunking
   ↓
Embedding Generation
   ↓
Vector Database
   ↓
Retriever
   ↓
LLM
   ↓
Final Answer

From a data engineering perspective, everything before the LLM is your domain.

Step 1: Data Ingestion (Where RAG Usually Breaks First)

Your data might come from: - PDFs - Databases - APIs - Notion / Confluence - Git repositories

Key problems to solve:

Incremental ingestion (not full reloads)
Document versioning
Deletions and updates
Metadata extraction

Bad RAG starts with bad ingestion.

Step 2: Chunking (The Most Underrated Decision)

You cannot embed entire documents.
You must split them into chunks.

Rule of thumb: Start with 500–800 tokens, then measure retrieval quality.

Step 3: Embeddings (Turning Text into Vectors)

Embeddings convert text into numeric vectors that capture semantic meaning.

Data engineering concerns: - Cost - Re-embedding strategy - Batch vs streaming

Step 4: Vector Databases

Vector databases store embeddings and allow similarity search.

What actually matters: - Metadata filtering - Index rebuild time - Hybrid search - Scalability

Step 5: Retrieval

Retrieval decides what context the LLM sees.

Retrieval quality matters more than the model you use.

Where Data Engineers Add the Most Value

Reliable ingestion pipelines
Incremental updates
Monitoring retrieval quality
Cost control

Common RAG Anti-Patterns

❌ Treating RAG as “just an LLM feature”
❌ No re-indexing strategy
❌ Ignoring latency
❌ No evaluation

Final Thoughts

RAG is not an AI problem.
It’s a data architecture problem with an LLM at the end.

Rag Practical Guide