What Is a RAG Pipeline? Definition and Guide

What Is a RAG Pipeline?

RAG (Retrieval-Augmented Generation) is an AI architecture that combines document retrieval with language model generation. Instead of relying solely on an LLM’s training data, a RAG pipeline retrieves relevant documents and feeds them as context to the model — producing answers that are grounded in your actual data.

How It Works

A RAG pipeline has two phases:

Indexing (offline):

Document ingestion — PDFs, web pages, and other documents are loaded
Extraction — content is extracted from raw documents into clean text
Chunking — text is split into segments sized for embedding models (typically 256-1024 tokens)
Embedding — each chunk is converted into a vector using an embedding model
Storage — vectors are stored in a vector database (Pinecone, Qdrant, ChromaDB, etc.)

Retrieval + Generation (online):

Query embedding — the user’s question is converted to a vector
Similarity search — the vector database finds the most relevant document chunks
Context assembly — retrieved chunks are formatted as context for the LLM
Generation — the LLM produces an answer grounded in the retrieved context

Why It Matters

RAG solves the fundamental limitations of standalone LLMs:

Knowledge freshness — RAG accesses current documents, not just training data
Source attribution — answers can cite specific documents and passages
Domain specificity — RAG works with private, proprietary, or specialized documents
Reduced hallucination — grounding answers in retrieved documents improves factual accuracy
Cost efficiency — cheaper than fine-tuning models on your data

RAG is the dominant architecture for enterprise AI assistants, document Q&A, customer support bots, and internal knowledge tools.

How pdfmux Fits in RAG Pipelines

pdfmux handles the critical first step: extracting clean, structured content from PDFs. Poor extraction leads to poor retrieval, which leads to poor answers. pdfmux ensures your RAG pipeline starts with high-quality data:

import pdfmux

# Step 1: Extract from PDF
result = pdfmux.convert("knowledge-base.pdf")

# Step 2: Chunk for embedding
chunks = result.chunks(max_tokens=512)

# Step 3: Embed and store (using your vector DB)
for chunk in chunks:
    vector = embed(chunk.text)
    db.upsert(vector, metadata=chunk.metadata)

pdfmux’s structured output preserves table data, headings, and document hierarchy — meaning your RAG pipeline retrieves more relevant, better-organized chunks.

Document Ingestion — loading and preparing documents for processing
Vector Embedding — converting text into numerical vectors for similarity search
Text Chunking — splitting documents into appropriately sized segments

FAQ

What’s the difference between RAG and fine-tuning?

Fine-tuning modifies the model’s weights using your data. RAG keeps the model unchanged and retrieves relevant context at query time. RAG is cheaper, doesn’t require ML expertise, and works with data that changes frequently. Fine-tuning is better for teaching the model new behaviors or formats.

What makes a good RAG pipeline?

Three things: clean extraction (garbage in, garbage out), smart chunking (right-sized segments that preserve context), and effective retrieval (embedding models and search strategies that find relevant content). pdfmux addresses the first two.

How do I evaluate RAG quality?

Measure retrieval precision (are the right documents found?), answer faithfulness (is the answer supported by retrieved context?), and answer relevance (does it address the question?). Frameworks like RAGAS and TruLens provide automated evaluation.

What Is a RAG Pipeline? Definition and Guide