Building a RAG Pipeline

This topic walks through building a simple RAG pipeline from start to finish, using plain steps instead of code. The goal is understanding the moving parts before touching any specific tool, so the ideas transfer cleanly no matter which platform gets used later.

The Six Building Blocks

Block	Purpose
Document loader	Reads raw files such as PDFs or web pages into plain text
Chunker	Splits the text into smaller, searchable pieces
Embedding model	Converts each chunk into numbers representing its meaning
Vector store	Saves the chunks and their numbers for fast searching
Retriever	Finds the closest matching chunks for a new question
Language model	Writes the final answer using the retrieved chunks

A Bakery Assembly Line Analogy

Flour arrives at a bakery loading dock, representing the document loader. Workers portion the dough into equal pieces, representing the chunker. Each piece gets a label describing its type, representing the embedding step. Labeled dough goes onto shelves, representing the vector store. A baker grabs the right labeled piece when an order comes in, representing the retriever. The oven finishes the product, representing the final language model step.

The Bakery Line Mapped to the Pipeline

Building the Pipeline Step by Step

1. Collect the source documents, such as manuals, FAQs, or policy pages.
2. Load the documents and clean up formatting issues like stray symbols.
3. Split each document into medium-sized chunks with small overlaps.
4. Run each chunk through an embedding model to get its number row.
5. Store every chunk and its number row inside a vector store.
6. When a question arrives, embed the question and search the vector store.
7. Send the top matching chunks and the original question to the language model.
8. Return the model's finished answer to the user.

A Worked Example: A Small FAQ Bot

A small business owner has a ten-page FAQ document. This owner loads that document, splits it into twenty small chunks by question and answer pairs, and embeds each chunk. A customer asks about shipping times. The system embeds this question, finds the shipping-related chunk, and sends it to the model, which replies with the correct shipping timeframe.

The FAQ Bot Example End to End

Common Beginner Mistakes

Mistake	Why It Hurts Results
Skipping chunk overlap	Important context gets cut off mid-idea
Using chunks that are too large	Irrelevant text dilutes the match quality
Forgetting to update the vector store after document changes	Answers stay outdated even after documents get fixed

Testing the Pipeline Before Launch

Run a batch of real sample questions through the pipeline before releasing it to users. Check whether the retrieved chunks actually match each question's intent. Adjust chunk size, overlap, or the embedding model based on these test results, rather than assuming everything works correctly on the first try.