5 Fun RAG Projects for Absolute Beginners

Image by Author | Canva

We all know the two major problems that have been pointed out as the main drawbacks of large language models (LLMs):

1. Hallucinations
2. Lack of updated information beyond their knowledge cutoff

Both of these issues raised serious doubts about the reliability of LLM outputs, and that’s where Retrieval-Augmented Generation (RAG) emerged as a powerful way to address them, offering more accurate, context-aware responses. Nowadays, it’s being used widely across various industries. However, many beginners get stuck exploring just one simple architecture: basic vector search over text documents. Sure, this works for most basic needs, but it limits creativity and understanding.

This article takes a different approach. Instead of a deep dive into a single, narrow setup to explain the details of one RAG application (like advanced prompting, chunking, embeddings, and retrieval), I believe beginners benefit more from exploring a broad spectrum of RAG patterns first. This way, you’ll see how adaptable and versatile the RAG concept really is and get inspired to create your own unique projects. So, let’s take a look at five fun and engaging projects I’ve prepared that will help you do just that. Let’s get started!

# 1. Building a RAG Application Using an Open-Source Model

 
Link: https://www.youtube.com/watch?v=HRvyei7vFSM

Start with the fundamentals by building a straightforward RAG system. This beginner-friendly project shows you how to build a RAG system that answers questions from any PDF using an open-source model like Llama2 without paid APIs. You will run Llama2 locally with Ollama, load and split PDFs using PyPDF from LangChain, create embeddings, and store them in an in-memory vector store like DocArray. Then, you will set up a retrieval chain in LangChain to fetch relevant chunks and generate answers. Along the way, you’ll learn the basics of working with local models, building retrieval pipelines, and testing outputs. The end result is a simple Q&A bot that can answer PDF-specific questions like “What’s the course cost?” with accurate context.

# 2. Multimodal RAG: Chatting with PDFs Containing Images and Tables

 
Link: https://youtu.be/uLrReyH5cu0?feature=shared

In the previous project, we only worked with text-based data. Now it’s time to level up. Multimodal RAG extends traditional systems to process images, tables, and text in PDFs. In this tutorial, Alejandro AO walks you through using tools like LangChain and the Unstructured library to process mixed content and feed it into a multimodal LLM (e.g., GPT-4 with vision). You’ll learn how to extract and embed text, images, and tables, combine them into a unified prompt, and generate answers that understand context across all formats. The embeddings will be stored in a vector database, and a LangChain retrieval chain will connect everything so you can ask questions like “Explain the chart on page 5.”

# 3. Creating an On-Device RAG with ObjectBox and LangChain

 
Link: https://www.youtube.com/watch?v=9LewL1bUS6g

Now, let’s go fully local. This project walks you through building a RAG system that runs entirely on your device (no cloud, no internet). In this tutorial, you’ll learn how to store your data and embeddings locally using the lightweight, ultra-efficient ObjectBox vector database. You’ll use LangChain to build the retrieval and generation pipeline so your model can answer questions from your documents directly on your machine. This is perfect for anyone concerned about privacy, data control, or just wanting to avoid API costs. In the end, you’ll have an AI Q&A system that lives on your device, responding quickly and securely.

# 4. Building a Real-Time RAG Pipeline with Neo4j and LangChain

 
Link: https://www.youtube.com/watch?v=Ik8gNjJ-13I

In this project, you’ll move from plain documents to powerful graphs. This tutorial shows you how to build a real-time RAG system using a knowledge graph backend. You’ll work in a notebook (like Colab), set up a Neo4j cloud instance, and create nodes and edges to represent your data. Then, using LangChain, you’ll connect your graph to an LLM for generation and retrieval, letting you query contextual relationships and visualize results. It’s a great way to learn graph logic, Cypher querying, and how to merge structured graph knowledge with smart AI answers. I’ve also written an in-depth guide on this topic, Building a Graph RAG System: A Step-by-Step Approach, where I break down how to create a GraphRAG setup from scratch. Do check that out as well if you prefer article-based tutorials.

# 5. Implementing Agentic RAG with Llama-Index

 
Link: https://youtube.com/playlist?list=PLU7aW4OZeUzxrJAdVRiadrBAMASwFalA0&feature=shared

In the earlier projects we focused on retrieval and generation, but here the goal is to make RAG “agentic” by giving it reasoning loops and tools so it can solve problems in multiple steps. This playlist by Prince Krampah is divided into 4 stages:

1. Router Query Engine: Configure Llama-Index to route questions to the right source, like a vector index vs. a summary index
2. Function Calling: Add tools like calculators or APIs so your RAG can pull in live data or perform tasks on the fly
3. Multi-Step Reasoning: Break down complex queries into smaller subtasks (“summarize first, then analyze”)
4. Over Multiple Documents: Scale your reasoning across several documents at once with agents handling sub-queries

It’s a hands-on journey that starts with basic agents and gradually adds more powerful capabilities using Llama-Index and open-source LLMs. By the end, you’ll have a RAG system that doesn’t just fetch answers, but actually thinks through problems step-by-step — even across multiple PDFs. You can also access the series on Medium in the form of articles for easier reference.

# Wrapping Up

 
And there you have it: 5 beginner-friendly RAG projects that go beyond the usual “vector search over text” setup. My advice? Don’t aim for perfection on your first try. Pick one project, follow along, and let yourself experiment. The more patterns you explore, the easier it’ll be to mix and match ideas for your own custom RAG applications. Remember that the real fun starts when you stop just “retrieving” and start “thinking” about how your AI can reason, adapt, and interact in smarter ways.
 
 

Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.