RAG Pipeline Projects That Interviewers Actually Want to See

When I first began working on RAG projects, I soon saw that just linking an LLM to a vector database was only the start. Building a simple RAG pipeline with a document loader, embedding model, vector database, and LLM is great for learning the basics. However, it doesn’t really prove you know how today’s AI systems deal with complex data, retrieval, reasoning, or real-world users.

Now, when I consider RAG projects, I look for something extra. In this article, I’ll walk you through four RAG pipeline projects that interviewers really want to see.

RAG Pipeline Projects

Here are four RAG pipeline projects I suggest building to make your AI Engineer portfolio stronger.

1. Build a Multi-Modal RAG Pipeline

The first project I recommend is a Multi-Modal RAG Pipeline.

Traditional RAG usually focuses on text. You load documents, break them into chunks, create embeddings, find relevant parts, and send them to an LLM.

But real-world documents are rarely only text. They often include images, tables, charts, diagrams, screenshots, PDFs, and other visuals. Modern multimodal RAG systems are built to find and use all these types of information, not just the text.

For this project, you’ll build a pipeline that can take in documents with both text and visuals, pull out useful details, index them, and find the right context for a user’s question.

2. Build an Agentic RAG Pipeline

The second project goes a step further: Agentic RAG.

I found this direction especially interesting because the retrieval process isn’t fixed anymore.

In a traditional RAG pipeline, the steps are usually simple: you ask a question, the system finds relevant parts, and then it generates an answer.

With Agentic RAG, an AI agent can choose when to retrieve information, which source or tool to use, if the information is enough, and if it needs to search again. This setup works much better for complex, multi-step questions.

Preparing for AI/ML Interviews?
If you’re building projects like these to boost your portfolio, you’ll also need to explain your choices in interviews. I recommend checking out Cracking Your First AI/ML Interview if you’re getting ready for your first interviews.

3. Build a Multi-Language RAG Pipeline

The third project I recommend is a Multi-Language RAG Pipeline.

This project is interesting because multilingual RAG brings challenges that are easy to miss. For example, your documents might be in English, Hindi, Spanish, or other languages, and users can ask questions in different languages too.

A basic monolingual retrieval system might have trouble if the question and the document are in different languages. A multilingual RAG setup needs to handle language differences during indexing, retrieval, reranking, and generation.

For this project, you’ll build a system that can find relevant information in any language and give the final answer in the user’s chosen language.

4. Create a ChatGPT for Your Personal Documents

Finally, I suggest building something simple to understand but powerful enough to show the full RAG workflow: a ChatGPT for your personal documents.

This project lets users upload their own documents and chat with them using a conversational interface.

The pipeline can take in documents, extract text, split it into chunks, create embeddings, search vectors, retrieve information, and generate responses with an LLM.

This project stands out because it feels like a real product, not just a one-off experiment.

The Takeaway

So, here are four RAG pipeline projects that interviewers really want to see:

Multi-Modal RAG proves you can handle complex, mixed data. Agentic RAG shows you can get dynamic retrieval and AI agent workflows. Multi-Language RAG demonstrates you can work with different languages. Personal Document RAG shows you can turn RAG into a real, usable app.

I hope you enjoyed this article on four RAG pipeline projects that interviewers want to see. For more AI and machine learning tips, you can follow me on Instagram.