A hands-on, end-to-end RAG pipeline example for developers: ingestion, chunking, retrieval and generation, with copy-paste code you can adapt.

Disclosure: Some links in this post are affiliate links. If you buy through them, Catai may earn a commission at no extra cost to you. We only recommend products we have tested.

Retrieval-Augmented Generation (RAG) sounds intimidating until you build one end to end. In this walkthrough you will assemble a small but complete pipeline: load documents, split them into sensible chunks, embed and store them, retrieve the most relevant passages for a question, and hand those passages to a language model to produce a grounded answer. By the end you will have a mental model you can reuse for almost any knowledge-base assistant.

What you’ll build

A single-file pipeline that answers questions about your own documents. It reads plain-text or Markdown files, keeps a local vector index, and returns answers that cite the source passages they came from — so you can trust and debug the output.

Prerequisites

You should be comfortable running a script and installing packages. Nothing here requires a GPU; a laptop is plenty for a few hundred documents. If you want to scale past that, you can rent cloud GPU compute for AI side projects later, but it is not needed to follow along.

Step 1 — Ingest and normalize

Read each document, strip boilerplate, and attach metadata (source path, title, date). Clean input is the single biggest lever on final answer quality — a noisy corpus produces noisy retrieval no matter how good your model is.

Step 2 — Chunk with intent

Do not split blindly by character count. Split on structure first (headings, paragraphs), then merge small pieces up to a target size with a little overlap so context is not cut mid-sentence. The trade-offs here matter enough that we compare approaches in chunking strategies that actually improve RAG retrieval.

Step 3 — Embed, store, retrieve, generate

Embed each chunk once, store the vectors, and at query time embed the question, pull the top-k nearest chunks, and pass them plus the question to the model. Always include the retrieved sources in the prompt and ask the model to answer only from them.

Common pitfalls

  • Chunks too large: retrieval returns one giant blob and precision collapses.
  • No overlap: answers get truncated at chunk boundaries.
  • No source display: you cannot tell hallucination from grounded fact.

Wrap-up

A working RAG pipeline is five honest steps, not magic. Nail ingestion and chunking and the rest falls into place. When you are ready to put this in front of real users, read how others did it in production.

Related reads