RAG Pipeline Explained! Building a RAG application is easy. Building one that de

By Greg Coquillo · November 7, 2026 · Curated by George's Blog

RAG Pipeline Explained!

Building a RAG application is easy.

Building one that delivers accurate, reliable, and source-backed answers is a completely different challenge.

A production-ready RAG system needs much more than a vector database and an LLM.

Here's the complete 9-step RAG pipeline:

INDEXING PIPELINE

1. Load & Parse

Extract clean text from PDFs, documents, and websites while preserving important metadata.

2. Chunk

Split large documents into smaller, meaningful sections to improve retrieval accuracy.

3. Embed

Convert text chunks into numerical vectors that capture semantic meaning.

4. Store & Index

Store embeddings in vector databases like Pinecone, Qdrant, or Weaviate for efficient searching.

QUERY PIPELINE

5. Rewrite Query

Transform user questions into search-friendly queries to improve retrieval relevance.

6. Retrieve

Find relevant documents using vector search, keyword search, or hybrid retrieval.

7. Rerank

Score retrieved documents and select the strongest matches before passing context to the LLM.

8. Generate

Use models like GPT, Claude, or Gemini to generate grounded answers with source citations.

EVALUATION PIPELINE

9. Evaluate & Monitor

Measure retrieval quality, answer faithfulness, latency, and cost using tools like Ragas and LangSmith.

The biggest lesson?

Better RAG performance doesn't always require a better LLM.

Sometimes, fixing your chunking strategy, retrieval logic, or reranking process makes a much bigger difference.

Bad chunks in = bad answers out.

♻️ Repost to help developers understand how production-ready RAG systems work.