Retrieval Augmented Generation (RAG): A Complete Guide

Learning Objectives

• Define Retrieval Augmented Generation (RAG) and understand why it matters.
• Break down the core building blocks: retriever, knowledge store, generator, and ranker.
• Build a simple but production-ready RAG pipeline in Python (≈60 lines of code).
• Apply RAG to real-world scenarios such as customer support, analytics Q&A, and document summarization.
• Recognize common mistakes—like knowledge drift and vector store bloat—and learn proven fixes.
• Discover how Galaxy’s SQL editor can act as a knowledge engine for RAG when your ground truth lives in a relational database.

1. Introduction

Large language models (LLMs) are powerful, but they have two glaring weaknesses:

1. Stale knowledge. The model only knows what it saw during training.
2. Hallucinations. When unsure, it may invent an answer that sounds plausible.

Retrieval Augmented Generation (RAG) solves both problems by injecting fresh, factual context into the model at inference time. Instead of relying solely on the model’s parameters, RAG retrieves relevant information from an external knowledge base (KB) and feeds that text—often called context—to the generator. The model grounds its response in that context and can cite sources, boosting accuracy and trust.

2. Why RAG Is a Game-Changer

• Up-to-date answers: Swap or update the KB without retraining the LLM.
• Explainability: Surface references so users know where facts came from.
• Scalability: Vector databases handle millions of documents with millisecond retrieval.
• Cost efficiency: Fine-tuning a foundation model on your entire corpus can be expensive; RAG is cheaper and easier to iterate.

3. Core Components of a RAG System

3.1 Knowledge Store

The canonical choice is a vector database (Pinecone, Weaviate, FAISS, etc.). Each document or chunk is embedded into a high-dimensional vector using an embedding model such as text-embedding-3-small or all-MiniLM-L6-v2. For SQL datasets, you can precompute embeddings for column descriptions or query results and store them alongside IDs.

3.2 Retriever

Given a user query, the retriever converts it into an embedding and performs nearest-neighbor search against the vector store, returning the k most similar chunks.

3.3 Generator

An LLM (GPT-4o, Claude 3, Llama 3, etc.) receives the retrieved chunks as system or user context, then generates the answer. This step is sometimes called sequence generation.

3.4 Ranker & Re-ranker (Optional)

Some stacks include a separate cross-encoder model to score passages more precisely or to reorder retrieved chunks before final generation.

4. Step-by-Step Tutorial: Build Your First RAG Pipeline

The following 12-minute exercise walks you through a minimal yet production-ready RAG loop. We’ll use:

• Python 3.11
• Hugging Face transformers + sentence-transformers
• FAISS for in-memory vector search (you can swap Pinecone for prod)
• OpenAI GPT-4o as the generator

pip install transformers sentence-transformers faiss-cpu openai tiktoken python-dotenv

Step 1. Load and Chunk Documents

import glob, textwrap, tiktoken
from pathlib import Path

def chunk_text(text, chunk_size=400):
wrapper = textwrap.TextWrapper(width=chunk_size, break_long_words=False)
return wrapper.wrap(text)

files = glob.glob("./docs/*.txt")
corpus = []
for f in files:
txt = Path(f).read_text()
corpus.extend(chunk_text(txt))
print(f"Loaded & chunked {len(corpus)} passages")

Step 2. Embed All Passages

from sentence_transformers import SentenceTransformer
import faiss, numpy as np

embedder = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embedder.encode(corpus, show_progress_bar=True)

index = faiss.IndexFlatIP(embeddings.shape[1])
index.add(np.array(embeddings).astype("float32"))

Step 3. Build the Retrieval Function

def retrieve(query, k=4):
q_emb = embedder.encode([query])
D, I = index.search(np.array(q_emb).astype("float32"), k)
return [corpus[i] for i in I[0]]

Step 4. Generate Answer with Retrieved Context

import os, openai
openai.api_key = os.getenv("OPENAI_API_KEY")

def rag_answer(question):
context = retrieve(question)
prompt = (
"You are a helpful assistant. Use ONLY the context below to answer the question.\n" +
"\n--- CONTEXT ---\n" + "\n".join(context) +
"\n--- QUESTION ---\n" + question +
"\n--- ANSWER ---\n"
)
resp = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[{"role":"user", "content": prompt}],
temperature=0.2,
)
return resp.choices[0].message.content.strip()

print(rag_answer("What were the key findings in the 2023 retention report?"))

Voilà! You now have a basic RAG system. Swap FAISS for a managed vector DB, add citation formatting, and you can go to production.

5. Hands-On Exercise

Try extending the example:

1. Replace text files with Markdown notes exported from your company wiki.
2. Chunk by semantic headings instead of fixed length.
3. Add a “source” field for each passage and instruct the LLM to cite URLs.
4. Log query, retrieved IDs, and final answer to a spreadsheet for evaluation.

6. Real-World Applications

• Customer Support Copilot: Ingest Zendesk tickets + FAQ docs; the bot drafts accurate responses and links to policy pages.
• SQL Knowledge Assistant: Index endorsed queries from Galaxy and let product managers ask natural-language questions that compile into reusable SQL.
• Regulatory Compliance: Lawyers fetch up-to-date statutes and generate summaries with citations.
• Data Catalog Q&A: Analysts query column definitions and lineage across thousands of tables.

7. Common Pitfalls & Troubleshooting

• Hallucinations Persist: Ensure retrieved context actually answers the question; add a post-filter that checks semantic similarity.
• Vector Drift: Re-embed content after large wording changes; automate a nightly batch.
• Latency Spikes: Use Max Marginal Relevance (MMR) to reduce k while maintaining diversity.
• Over-long Prompts: Token limits matter. Summarize or select top passages (<= 4) to stay under 8 k tokens.

8. Best Practices

1. Chunk smart, not arbitrary. Split by semantic boundaries—headings, paragraphs, or SQL CTEs—to preserve context.
2. Store metadata. Include title, author, date, and tags for filtering.
3. Track feedback loops. Log user choices so you can fine-tune retrieval weightings.
4. Secure your KB. Apply row-level permissions and audit logs, especially for PII-laden docs.
5. Version your embeddings. When upgrading an embedding model, re-index in parallel, A/B test, then cut over.

9. Practicing RAG with Galaxy

If your organization’s source of truth is SQL, Galaxy becomes a powerful bridge:

• Embed Endorsed Queries: Use Galaxy’s API to export endorsed SQL, strip comments into descriptions, and batch-embed them.
• Dynamic Retrieval: Call Galaxy’s REST endpoint to run live queries as part of the retrieval step, giving the LLM real-time numbers (e.g., latest MRR).
• Semantic Layer Alignment: Because Galaxy stores metrics definitions centrally, you avoid multiple “active user” definitions creeping into your RAG system.
• Role-Based Access: Galaxy’s permissioning ensures only safe data is exposed to the LLM, reducing compliance risks.

Example: ask "How many paying customers churned last week and what are the top 3 reasons?". The retriever pulls the validated “churn” SQL from Galaxy, executes it, joins with a sentiment table, and returns a numeric table and the raw JSON. The generator then crafts a narrative answer citing the query link hosted in Galaxy.

10. Key Takeaways & Next Steps

• RAG enhances LLMs with fresh, verifiable knowledge without retraining.
• Core loop = Embed → Retrieve → Generate. Each step has tunable knobs.
• Production readiness demands chunking strategy, metadata, evaluation, and security.
• Galaxy can supply a battle-tested SQL knowledge base, making your RAG stack both accurate and compliant.
• Next: explore hybrid retrieval (BM25 + vectors), experiment with re-rankers like Cohere Rerank, and build evaluation harnesses (ragas, truLens).