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RAG & Knowledge Systems

Ragbook Notebooks

Ragbook Notebooks is a public GitHub collection of Jupyter notebooks accompanying Towards AI’s RAG book, with examples covering transformer concepts, prompt engineering, LLM application patterns, RAG, agents and fine-tuning.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ragbook Notebooks — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the collection when you want a chapter-organized notebook example for a specific LLM or RAG topic, or are choosing practical material for instruction.

Who it is for

  • Learners exploring LLM and RAG concepts through notebook examples.
  • Educators selecting hands-on material for an LLM application course.

Documented uses

  • Study transformer architectures and prompt engineering.
  • Explore example application patterns using LangChain or LlamaIndex.
  • Find notebook examples on RAG evaluation, agents, knowledge graphs and fine-tuning.
Before you choose

Requirements & limits

The repository is public, and the README links selected notebooks to Google Colab.

  • The README names notebooks by chapter and topic, making it possible to identify examples relevant to a particular lesson.
  • Some listed notebooks have Google Colab links.
  • The README does not specify execution requirements for each notebook, so check the individual notebook before planning a hands-on session.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Choose and open a RAG book notebook

Use the repository index to select a notebook by chapter and open its linked Colab notebook.

Before you start

  • Access to the public GitHub repository README.
  • A notebook topic from the listed chapters, such as transformer architectures, prompt engineering, LangChain, LlamaIndex, or advanced RAG.

Steps

  1. Open the repository README.

  2. Find the chapter and notebook topic you want to study.

  3. Select that notebook's Google Colab link in the README.

Expected outcome

The selected notebook is opened through its linked Google Colab page.

Guide limitations

  • The README lists notebooks but does not document runtime setup, required credentials, execution steps, or notebook results.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
towardsai/ragbook-notebooks · Official homepage title · retained in its source language
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