When you’re learning something new, projects are super important. They help you turn theory into practice and really understand what you’re doing. Guided projects are even better because they give you a clear path to follow. Experts show you the way, so you don’t get lost or make rookie mistakes. In this blog, we’ve got five awesome RAG projects that you definitely need to check out in 2025. Whether you’re new to RAG or already know your way around, these solved RAG projects will help you level up. Let’s get started!
Table of contents
What is RAG?
RAG, or Retrieval-Augmented Generation, is a powerful approach in AI that combines retrieval mechanisms with generative models. It retrieves relevant information from large datasets and uses that context to generate accurate and contextually relevant responses. This hybrid method enhances the performance of AI systems, making them more reliable and efficient for tasks like question-answering and content generation.
To know more, read our detailed article on RAG!
Now, let’s look at the top 5 solved RAG projects.
Document Retriever Search Engine with LangChain
Build a powerful document retrieval search engine using LangChain. Learn to process Wikipedia data, chunk documents, generate embeddings, and index them in a vector database. Optimize retrieval workflows for efficiency and explore advanced retriever methods.
This project is ideal for intermediate-level learners with a background in AI and NLP. It’s perfect for those looking to enhance their expertise in AI-driven QA systems, explore the capabilities of LangChain, and master advanced frameworks for real-world applications.
Key Skills to Learn
- Indexing and querying document embeddings
- Processing and chunking large documents
- Generating and optimizing embeddings
- Using vector databases for efficient retrieval
- Implementing advanced retriever methods
How to Solve?
- Process and Chunk Documents: Learn to process Wikipedia data and split documents into manageable chunks.
- Generate Embeddings: Create embeddings for document chunks to capture semantic meaning.
- Index Data: Use vector databases to index embeddings for efficient similarity searches.
- Optimize Retrieval: Implement and optimize retrieval workflows to ensure efficient document retrieval.
- Advanced Methods: Explore advanced retriever methods and their applications in QA systems.
Find the solution to this RAG project here!
Collaborative Multi-Agent System with LangGraph
Learn to build a collaborative multi-agent system using LangGraph in this 30-minute intermediate-level course. Gain hands-on experience with LangGraph and understand the fundamentals of RAG and LlamaIndex.
This project is ideal for AI practitioners, software developers, and system architects aiming to deepen their understanding of multi-agent systems. It’s also perfect for learners enthusiastic about entering the world of collaborative AI systems and mastering LangGraph.
Key Skills to Learn
- Fundamentals of RAG and LlamaIndex
- Building RAG systems using LlamaIndex
- Hands-on training with LangGraph
- Creating collaborative multi-agent systems
How to Solve?
- Understand RAG and LlamaIndex: Learn the basics of RAG and how LlamaIndex can be used to build efficient systems.
- Build RAG Systems: Implement a RAG system using LlamaIndex, focusing on efficient data retrieval and processing.
- Hands-On with LangGraph: Use LangGraph to build a collaborative multi-agent system, leveraging its graph-based structures for efficient communication.
- Create Multi-Agent Systems: Develop a collaborative multi-agent system, focusing on node interactions, task outputs, and overall system coordination.
Find the solution to this RAG project here!
QA RAG system with LangChain
Build a QA RAG system using LangChain in this 30-minute intermediate-level course. Gain a deep understanding of RAG fundamentals and LangChain capabilities. Get hands-on experience in creating efficient QA systems.
Ideal for individuals looking to enhance their expertise in AI-driven QA systems and explore LangChain’s capabilities. Suitable for those on their journey to mastering AI and NLP, ready to dive into advanced frameworks.
Key Skills to Learn
- Fundamentals of RAG
- In-depth knowledge of LangChain
- Building QA RAG systems
- Integrating LLMs with vector databases
How to Solve?
- Understand RAG: Learn the basics of RAG and how it enhances QA systems.
- Master LangChain: Gain in-depth knowledge of LangChain and its tools for building generative AI applications.
- Build QA System: Create a QA RAG system, integrating an LLM with a vector database for efficient document retrieval.
- Hands-On Experience: Implement and test the QA system, ensuring it provides accurate and contextually relevant answers.
Find the solution to this RAG project here!
Agentic Corrective RAG System in LangGraph
Build an Agentic Corrective RAG System using LangGraph in this 30-minute intermediate-level course. Gain a solid foundation in LangGraph and learn to design self-correcting RAG systems. Engage in hands-on sessions to build your own corrective RAG system.
Ideal for individuals looking to enhance their expertise in AI-driven QA systems and explore LangGraph’s capabilities. Suitable for those on their journey to mastering AI and NLP, ready to dive into advanced frameworks.
Key Skills to Learn
- Fundamentals of LangGraph
- Designing self-correcting RAG systems
- Implementing corrective mechanisms
- Building and testing a corrective RAG system
How to Solve?
- Understand LangGraph: Learn the basics of LangGraph and its capabilities for building advanced AI systems.
- Design Self-Correcting RAG: Understand how to design a RAG system with self-correcting mechanisms.
- Implement Corrective Mechanisms: Implement corrective mechanisms to enhance the accuracy and reliability of the system.
- Hands-On Building: Engage in practical sessions to build and test your own corrective RAG system step-by-step.
Find the solution to this RAG project here!
End-to-end RAG Application Development with LangChain and Streamlit
Develop an end-to-end RAG application using LangChain and Streamlit in this 30-minute intermediate-level course. Learn the concepts of Retrieval-Augmented Generation (RAG) and gain hands-on experience with practical use cases. Build interactive and visually appealing apps using Streamlit.
Ideal for developers, data scientists, and AI enthusiasts who want to create advanced AI applications. Basic knowledge of Python and familiarity with LLMs is recommended.
Key Skills to Learn
- Concepts of Retrieval-Augmented Generation (RAG)
- Working with LangChain
- Building interactive apps with Streamlit
- Practical RAG use cases
How to Solve?
- Understand RAG: Learn the core concepts of Retrieval-Augmented Generation (RAG).
- Work with LangChain: Gain hands-on experience with LangChain for building RAG systems.
- Build with Streamlit: Create interactive and visually appealing apps using Streamlit.
- Practical Use Cases: Implement practical RAG use cases and build end-to-end applications.
Find the solution to this RAG project here!
Also Read: How to Become a RAG Specialist in 2025?
End Note
By tackling these projects, you’ll not only enhance your understanding of RAG systems but also gain practical skills that are essential in the field of AI and machine learning. Each project offers a unique challenge that will help you apply your knowledge in real-world scenarios and prepare you for advanced studies or career opportunities in AI.
Do you want us to add another solved RAG project here? Let us know the topic in the comment section below!
Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.
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